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Deep learning for biomedical data analysis : techniques, approaches, and applications / / Mourad Elloumi, editor
| Deep learning for biomedical data analysis : techniques, approaches, and applications / / Mourad Elloumi, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (358 pages) |
| Disciplina | 610.285 |
| Soggetto topico | Artificial intelligence - Medical applications |
| ISBN | 3-030-71676-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910492141203321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others]
| Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others] |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] |
| Descrizione fisica | 1 online resource (458 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Deep learning (Machine learning) |
| ISBN |
1-119-85798-8
1-119-85797-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- 1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science -- 1.1 Introduction -- 1.2 Drug Discovery, Screening and Repurposing -- 1.3 DL and Pharmaceutical Formulation Strategy -- 1.3.1 DL in Dose and Formulation Prediction -- 1.3.2 DL in Dissolution and Release Studies -- 1.3.3 DL in the Manufacturing Process -- 1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery -- 1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory -- 1.4.2 Artificial Intelligence and Drug Delivery Algorithms -- 1.4.3 Nanoinformatics -- 1.5 Model Prediction for Site-Specific Drug Delivery -- 1.5.1 Prediction of Mode and a Site-Specific Action -- 1.5.2 Precision Medicine -- 1.6 Future Scope and Challenges -- 1.7 Conclusion -- References -- 2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care -- 2.1 Introduction -- 2.2 IoT and WBAN in Healthcare Systems -- 2.2.1 IoT in Healthcare -- 2.2.2 WBAN -- 2.2.2.1 Key Features of Medical Networks in the Wireless Body Area -- 2.2.2.2 Data Transmission & -- Storage Health -- 2.2.2.3 Privacy and Security Concerns in Big Data -- 2.3 Blockchain Technology in Healthcare -- 2.3.1 Importance of Blockchain -- 2.3.2 Role of Blockchain in Healthcare -- 2.3.3 Benefits of Blockchain in Healthcare Applications -- 2.3.4 Elements of Blockchain -- 2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling -- 2.3.6 Mobile Health and Remote Monitoring -- 2.3.7 Different Mobile Health Application with Description of Usage in Area of Application -- 2.3.8 Patient-Centered Blockchain Mode -- 2.3.9 Electronic Medical Record -- 2.3.9.1 The Most Significant Barriers to Adoption Are.
2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology -- 2.4 Deep Learning in Healthcare -- 2.4.1 Deep Learning Models -- 2.4.1.1 Recurrent Neural Networks (RNN) -- 2.4.1.2 Convolutional Neural Networks (CNN) -- 2.4.1.3 Deep Belief Network (DBN) -- 2.4.1.4 Contrasts Between Models -- 2.4.1.5 Use of Deep Learning in Healthcare -- 2.5 Conclusion -- 2.6 Acknowledgments -- References -- 3 Deep Learning on Site-Specific Drug Delivery System -- 3.1 Introduction -- 3.2 Deep Learning -- 3.2.1 Types of Algorithms Used in Deep Learning -- 3.2.1.1 Convolutional Neural Networks (CNNs) -- 3.2.1.2 Long Short-Term Memory Networks (LSTMs) -- 3.2.1.3 Recurrent Neural Networks -- 3.2.1.4 Generative Adversarial Networks (GANs) -- 3.2.1.5 Radial Basis Function Networks -- 3.2.1.6 Multilayer Perceptron -- 3.2.1.7 Self-Organizing Maps -- 3.2.1.8 Deep Belief Networks -- 3.3 Machine Learning and Deep Learning Comparison -- 3.4 Applications of Deep Learning in Drug Delivery System -- 3.5 Conclusion -- References -- 4 Deep Learning Advancements in Target Delivery -- 4.1 Introduction: Deep Learning and Targeted Drug Delivery -- 4.2 Different Models/Approaches of Deep Learning and Targeting Drug -- 4.3 QSAR Model -- 4.3.1 Model of Deep Long-Term Short-Term Memory -- 4.3.2 RNN Model -- 4.3.3 CNN Model -- 4.4 Deep Learning Process Applications in Pharmaceutical -- 4.5 Techniques for Predicting Pharmacotherapy -- 4.6 Approach to Diagnosis -- 4.7 Application -- 4.7.1 Deep Learning in Drug Discovery -- 4.7.2 Medical Imaging and Deep Learning Process -- 4.7.3 Deep Learning in Diagnostic and Screening -- 4.7.4 Clinical Trials Using Deep Learning Models -- 4.7.5 Learning for Personalized Medicine -- 4.8 Conclusion -- Acknowledgment -- References -- 5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors. 5.1 Introduction -- 5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis -- 5.2.1 Gene Identification and Genome Data -- 5.2.2 Image Diagnosis -- 5.2.3 Radiomics, Radiogenomics, and Digital Biopsy -- 5.2.4 Medical Image Analysis in Mammography -- 5.2.5 Magnetic Resonance Imaging -- 5.2.6 CT Imaging -- 5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation -- 5.3.1 Next-Generation Sequencing -- 5.3.2 Biomarkers and Clinical Validation -- 5.4 DL and Translational Oncology -- 5.4.1 Prediction -- 5.4.2 Segmentation -- 5.4.3 Knowledge Graphs and Cancer Drug Repurposing -- 5.4.4 Automated Treatment Planning -- 5.4.5 Clinical Benefits -- 5.5 DL in Clinical Trials-A Necessary Paradigm Shift -- 5.6 Challenges and Limitations -- 5.7 Conclusion -- References -- 6 Personalized Therapy Using Deep Learning Advances -- 6.1 Introduction -- 6.2 Deep Learning -- 6.2.1 Convolutional Neural Networks -- 6.2.2 Autoencoders -- 6.2.3 Deep Belief Network (DBN) -- 6.2.4 Deep Reinforcement Learning -- 6.2.5 Generative Adversarial Network -- 6.2.6 Long Short-Term Memory Networks -- References -- 7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.2.1 Types of Artificial Intelligence -- 7.2.1.1 Machine Intelligence -- 7.2.1.2 Types of Machine Intelligence -- 7.2.2 Applications of Artificial Intelligence -- 7.2.2.1 Role in Healthcare Diagnostics -- 7.2.2.2 AI in Telehealth -- 7.2.2.3 Role in Structural Health Monitoring -- 7.2.2.4 Role in Remote Medicare Management -- 7.2.2.5 Predictive Analysis Using Big Data -- 7.2.2.6 AI's Role in Virtual Monitoring of Patients -- 7.2.2.7 Functions of Devices -- 7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring -- 7.2.2.9 Clinical Decision Support. 7.2.3 Utilization of Artificial Intelligence in Telemedicine -- 7.2.3.1 Artificial Intelligence-Assisted Telemedicine -- 7.2.3.2 Telehealth and New Care Models -- 7.2.3.3 Strategy of Telecare Domain -- 7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains -- 7.3 AI-Enabled Telehealth: Social and Ethical Considerations -- 7.4 Conclusion -- References -- 8 Deep Learning Framework for Cancer Diagnosis and Treatment -- 8.1 Deep Learning: An Emerging Field for Cancer Management -- 8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer -- 8.3 Applications of Deep Learning in Cancer Diagnosis -- 8.3.1 Medical Imaging Through Artificial Intelligence -- 8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning -- 8.3.3 Digital Pathology Through Deep Learning -- 8.3.4 Application of Artificial Intelligence in Surgery -- 8.3.5 Histopathological Images Using Deep Learning -- 8.3.6 MRI and Ultrasound Images Through Deep Learning -- 8.4 Clinical Applications of Deep Learning in the Management of Cancer -- 8.5 Ethical Considerations in Deep Learning-Based Robotic Therapy -- 8.6 Conclusion -- Acknowledgments -- References -- 9 Applications of Deep Learning in Radiation Therapy -- 9.1 Introduction -- 9.2 History of Radiotherapy -- 9.3 Principal of Radiotherapy -- 9.4 Deep Learning -- 9.5 Radiation Therapy Techniques -- 9.5.1 External Beam Radiation Therapy -- 9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) -- 9.5.3 Intensity Modulated Radiation Therapy (IMRT) -- 9.5.4 Image-Guided Radiation Therapy (IGRT) -- 9.5.5 Intraoperative Radiation Therapy (IORT) -- 9.5.6 Brachytherapy -- 9.5.7 Stereotactic Radiosurgery (SRS) -- 9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist -- 9.6.1 Deep Learning in Patient Assessment -- 9.6.1.1 Radiotherapy Results Prediction. 9.6.1.2 Respiratory Signal Prediction -- 9.6.2 Simulation Computed Tomography -- 9.6.3 Targets and Organs-at-Risk Segmentation -- 9.6.4 Treatment Planning -- 9.6.4.1 Beam Angle Optimization -- 9.6.4.2 Dose Prediction -- 9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists -- 9.7 Conclusion -- References -- 10 Application of Deep Learning in Radiation Therapy -- 10.1 Introduction -- 10.2 Radiotherapy -- 10.3 Principle of Deep Learning and Machine Learning -- 10.3.1 Deep Neural Networks (DNN) -- 10.3.2 Convolutional Neural Network -- 10.4 Role of AI and Deep Learning in Radiation Therapy -- 10.5 Platforms for Deep Learning and Tools for Radiotherapy -- 10.6 Radiation Therapy Implementation in Deep Learning -- 10.6.1 Deep Learning and Imaging Techniques -- 10.6.2 Image Segmentation -- 10.6.3 Lesion Segmentation -- 10.6.4 Computer-Aided Diagnosis -- 10.6.5 Computer-Aided Detection -- 10.6.6 Quality Assurance -- 10.6.7 Treatment Planning -- 10.6.8 Treatment Delivery -- 10.6.9 Response to Treatment -- 10.7 Prediction of Outcomes -- 10.7.1 Toxicity -- 10.7.2 Survival and the Ability to Respond -- 10.8 Deep Learning in Conjunction With Radiomoic -- 10.9 Planning for Treatment -- 10.9.1 Optimization of Beam Angle -- 10.9.2 Prediction of Dose -- 10.10 Deep Learning's Challenges and Future Potential -- 10.11 Conclusion -- References -- 11 Deep Learning Framework for Cancer -- 11.1 Introduction -- 11.2 Brief History of Deep Learning -- 11.3 Types of Deep Learning Methods -- 11.4 Applications of Deep Learning -- 11.4.1 Toxicity Detection for Different Chemical Structures -- 11.4.2 Mitosis Detection -- 11.4.3 Radiology or Medical Imaging -- 11.4.4 Hallucination -- 11.4.5 Next-Generation Sequencing (NGS) -- 11.4.6 Drug Discovery -- 11.4.7 Sequence or Video Generation -- 11.4.8 Other Applications -- 11.5 Cancer -- 11.5.1 Factors. 11.5.1.1 Heredity. |
| Record Nr. | UNINA-9910642709403321 |
| Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others]
| Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others] |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] |
| Descrizione fisica | 1 online resource (458 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Deep learning (Machine learning) |
| ISBN |
1-119-85798-8
1-119-85797-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- 1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science -- 1.1 Introduction -- 1.2 Drug Discovery, Screening and Repurposing -- 1.3 DL and Pharmaceutical Formulation Strategy -- 1.3.1 DL in Dose and Formulation Prediction -- 1.3.2 DL in Dissolution and Release Studies -- 1.3.3 DL in the Manufacturing Process -- 1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery -- 1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory -- 1.4.2 Artificial Intelligence and Drug Delivery Algorithms -- 1.4.3 Nanoinformatics -- 1.5 Model Prediction for Site-Specific Drug Delivery -- 1.5.1 Prediction of Mode and a Site-Specific Action -- 1.5.2 Precision Medicine -- 1.6 Future Scope and Challenges -- 1.7 Conclusion -- References -- 2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care -- 2.1 Introduction -- 2.2 IoT and WBAN in Healthcare Systems -- 2.2.1 IoT in Healthcare -- 2.2.2 WBAN -- 2.2.2.1 Key Features of Medical Networks in the Wireless Body Area -- 2.2.2.2 Data Transmission & -- Storage Health -- 2.2.2.3 Privacy and Security Concerns in Big Data -- 2.3 Blockchain Technology in Healthcare -- 2.3.1 Importance of Blockchain -- 2.3.2 Role of Blockchain in Healthcare -- 2.3.3 Benefits of Blockchain in Healthcare Applications -- 2.3.4 Elements of Blockchain -- 2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling -- 2.3.6 Mobile Health and Remote Monitoring -- 2.3.7 Different Mobile Health Application with Description of Usage in Area of Application -- 2.3.8 Patient-Centered Blockchain Mode -- 2.3.9 Electronic Medical Record -- 2.3.9.1 The Most Significant Barriers to Adoption Are.
2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology -- 2.4 Deep Learning in Healthcare -- 2.4.1 Deep Learning Models -- 2.4.1.1 Recurrent Neural Networks (RNN) -- 2.4.1.2 Convolutional Neural Networks (CNN) -- 2.4.1.3 Deep Belief Network (DBN) -- 2.4.1.4 Contrasts Between Models -- 2.4.1.5 Use of Deep Learning in Healthcare -- 2.5 Conclusion -- 2.6 Acknowledgments -- References -- 3 Deep Learning on Site-Specific Drug Delivery System -- 3.1 Introduction -- 3.2 Deep Learning -- 3.2.1 Types of Algorithms Used in Deep Learning -- 3.2.1.1 Convolutional Neural Networks (CNNs) -- 3.2.1.2 Long Short-Term Memory Networks (LSTMs) -- 3.2.1.3 Recurrent Neural Networks -- 3.2.1.4 Generative Adversarial Networks (GANs) -- 3.2.1.5 Radial Basis Function Networks -- 3.2.1.6 Multilayer Perceptron -- 3.2.1.7 Self-Organizing Maps -- 3.2.1.8 Deep Belief Networks -- 3.3 Machine Learning and Deep Learning Comparison -- 3.4 Applications of Deep Learning in Drug Delivery System -- 3.5 Conclusion -- References -- 4 Deep Learning Advancements in Target Delivery -- 4.1 Introduction: Deep Learning and Targeted Drug Delivery -- 4.2 Different Models/Approaches of Deep Learning and Targeting Drug -- 4.3 QSAR Model -- 4.3.1 Model of Deep Long-Term Short-Term Memory -- 4.3.2 RNN Model -- 4.3.3 CNN Model -- 4.4 Deep Learning Process Applications in Pharmaceutical -- 4.5 Techniques for Predicting Pharmacotherapy -- 4.6 Approach to Diagnosis -- 4.7 Application -- 4.7.1 Deep Learning in Drug Discovery -- 4.7.2 Medical Imaging and Deep Learning Process -- 4.7.3 Deep Learning in Diagnostic and Screening -- 4.7.4 Clinical Trials Using Deep Learning Models -- 4.7.5 Learning for Personalized Medicine -- 4.8 Conclusion -- Acknowledgment -- References -- 5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors. 5.1 Introduction -- 5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis -- 5.2.1 Gene Identification and Genome Data -- 5.2.2 Image Diagnosis -- 5.2.3 Radiomics, Radiogenomics, and Digital Biopsy -- 5.2.4 Medical Image Analysis in Mammography -- 5.2.5 Magnetic Resonance Imaging -- 5.2.6 CT Imaging -- 5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation -- 5.3.1 Next-Generation Sequencing -- 5.3.2 Biomarkers and Clinical Validation -- 5.4 DL and Translational Oncology -- 5.4.1 Prediction -- 5.4.2 Segmentation -- 5.4.3 Knowledge Graphs and Cancer Drug Repurposing -- 5.4.4 Automated Treatment Planning -- 5.4.5 Clinical Benefits -- 5.5 DL in Clinical Trials-A Necessary Paradigm Shift -- 5.6 Challenges and Limitations -- 5.7 Conclusion -- References -- 6 Personalized Therapy Using Deep Learning Advances -- 6.1 Introduction -- 6.2 Deep Learning -- 6.2.1 Convolutional Neural Networks -- 6.2.2 Autoencoders -- 6.2.3 Deep Belief Network (DBN) -- 6.2.4 Deep Reinforcement Learning -- 6.2.5 Generative Adversarial Network -- 6.2.6 Long Short-Term Memory Networks -- References -- 7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.2.1 Types of Artificial Intelligence -- 7.2.1.1 Machine Intelligence -- 7.2.1.2 Types of Machine Intelligence -- 7.2.2 Applications of Artificial Intelligence -- 7.2.2.1 Role in Healthcare Diagnostics -- 7.2.2.2 AI in Telehealth -- 7.2.2.3 Role in Structural Health Monitoring -- 7.2.2.4 Role in Remote Medicare Management -- 7.2.2.5 Predictive Analysis Using Big Data -- 7.2.2.6 AI's Role in Virtual Monitoring of Patients -- 7.2.2.7 Functions of Devices -- 7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring -- 7.2.2.9 Clinical Decision Support. 7.2.3 Utilization of Artificial Intelligence in Telemedicine -- 7.2.3.1 Artificial Intelligence-Assisted Telemedicine -- 7.2.3.2 Telehealth and New Care Models -- 7.2.3.3 Strategy of Telecare Domain -- 7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains -- 7.3 AI-Enabled Telehealth: Social and Ethical Considerations -- 7.4 Conclusion -- References -- 8 Deep Learning Framework for Cancer Diagnosis and Treatment -- 8.1 Deep Learning: An Emerging Field for Cancer Management -- 8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer -- 8.3 Applications of Deep Learning in Cancer Diagnosis -- 8.3.1 Medical Imaging Through Artificial Intelligence -- 8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning -- 8.3.3 Digital Pathology Through Deep Learning -- 8.3.4 Application of Artificial Intelligence in Surgery -- 8.3.5 Histopathological Images Using Deep Learning -- 8.3.6 MRI and Ultrasound Images Through Deep Learning -- 8.4 Clinical Applications of Deep Learning in the Management of Cancer -- 8.5 Ethical Considerations in Deep Learning-Based Robotic Therapy -- 8.6 Conclusion -- Acknowledgments -- References -- 9 Applications of Deep Learning in Radiation Therapy -- 9.1 Introduction -- 9.2 History of Radiotherapy -- 9.3 Principal of Radiotherapy -- 9.4 Deep Learning -- 9.5 Radiation Therapy Techniques -- 9.5.1 External Beam Radiation Therapy -- 9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) -- 9.5.3 Intensity Modulated Radiation Therapy (IMRT) -- 9.5.4 Image-Guided Radiation Therapy (IGRT) -- 9.5.5 Intraoperative Radiation Therapy (IORT) -- 9.5.6 Brachytherapy -- 9.5.7 Stereotactic Radiosurgery (SRS) -- 9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist -- 9.6.1 Deep Learning in Patient Assessment -- 9.6.1.1 Radiotherapy Results Prediction. 9.6.1.2 Respiratory Signal Prediction -- 9.6.2 Simulation Computed Tomography -- 9.6.3 Targets and Organs-at-Risk Segmentation -- 9.6.4 Treatment Planning -- 9.6.4.1 Beam Angle Optimization -- 9.6.4.2 Dose Prediction -- 9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists -- 9.7 Conclusion -- References -- 10 Application of Deep Learning in Radiation Therapy -- 10.1 Introduction -- 10.2 Radiotherapy -- 10.3 Principle of Deep Learning and Machine Learning -- 10.3.1 Deep Neural Networks (DNN) -- 10.3.2 Convolutional Neural Network -- 10.4 Role of AI and Deep Learning in Radiation Therapy -- 10.5 Platforms for Deep Learning and Tools for Radiotherapy -- 10.6 Radiation Therapy Implementation in Deep Learning -- 10.6.1 Deep Learning and Imaging Techniques -- 10.6.2 Image Segmentation -- 10.6.3 Lesion Segmentation -- 10.6.4 Computer-Aided Diagnosis -- 10.6.5 Computer-Aided Detection -- 10.6.6 Quality Assurance -- 10.6.7 Treatment Planning -- 10.6.8 Treatment Delivery -- 10.6.9 Response to Treatment -- 10.7 Prediction of Outcomes -- 10.7.1 Toxicity -- 10.7.2 Survival and the Ability to Respond -- 10.8 Deep Learning in Conjunction With Radiomoic -- 10.9 Planning for Treatment -- 10.9.1 Optimization of Beam Angle -- 10.9.2 Prediction of Dose -- 10.10 Deep Learning's Challenges and Future Potential -- 10.11 Conclusion -- References -- 11 Deep Learning Framework for Cancer -- 11.1 Introduction -- 11.2 Brief History of Deep Learning -- 11.3 Types of Deep Learning Methods -- 11.4 Applications of Deep Learning -- 11.4.1 Toxicity Detection for Different Chemical Structures -- 11.4.2 Mitosis Detection -- 11.4.3 Radiology or Medical Imaging -- 11.4.4 Hallucination -- 11.4.5 Next-Generation Sequencing (NGS) -- 11.4.6 Drug Discovery -- 11.4.7 Sequence or Video Generation -- 11.4.8 Other Applications -- 11.5 Cancer -- 11.5.1 Factors. 11.5.1.1 Heredity. |
| Record Nr. | UNINA-9910831186303321 |
| Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deployable multimodal machine intelligence : applications in biomedical engineering / / Hongliang Ren
| Deployable multimodal machine intelligence : applications in biomedical engineering / / Hongliang Ren |
| Autore | Ren Hongliang |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (589 pages) |
| Disciplina | 610.285 |
| Collana | Lecture notes in bioengineering |
| Soggetto topico |
Artificial intelligence - Medical applications
Biomimetics Robotics in medicine |
| ISBN |
9789811959325
9789811959318 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | reviews orimimetic deployable mechanisms with potential functionalities in biomedical robotics -- Deployable and interchangeable telescoping tubes actuated with multiple tendons -- Deployable and foldable parallelogram mechanism for generating remote center of motion -- Origami Bending and Bistability for Transoral procedures -- Force-sensitive origami trihexaflexagon gripper actuated by foldable pneumatic bellows -- Untethered Inflatable Origami -- Wormigami and Tippysaurus origami structures -- Multi-leg insect-size soft foldable robots -- Magnetically Actuated Luminal Origami (MALO) -- Compressable and steerable Slinky motions -- Electromagnetically actuated origami structures for untethered optical steering -- Untethered soft ferromagnetic quad-jaws cootie catcher with selectively coupled degrees of freedom -- Wearable Origami Rendering Mechanism (WORM) for aspiring haptic illusions -- Wearable Compression-aware Force Rendering (CAFR) with deployable compression generating and sensing. These multi-DOF deployable robots integrated tactile interface sensing and multimodal actuation -- Kinesthesia sensorization of foldable tubular designs using soft sensors -- Flat Foldable Kirigami for Chipless Wireless Sensing -- Deployable kirigami for intra-abdominal monitoring -- Stretchable Strain Sensors by Kirigami Deployable on Balloons with Temporary Tattoo Paper -- Multi-DOF proprioceptive origami structures with fiducial markers and computer vision-based optical tracking -- Multimodal robotic deployable mechanisms with intelligent perception capabilities. . |
| Record Nr. | UNINA-9910647783203321 |
Ren Hongliang
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| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digital health and medical analytics : second international conference, DHA 2020, Beijing, China, July 25, 2020 : revised selected papers / / edited by Yichuan Wang [and four others]
| Digital health and medical analytics : second international conference, DHA 2020, Beijing, China, July 25, 2020 : revised selected papers / / edited by Yichuan Wang [and four others] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (144 pages) |
| Disciplina | 610.285 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Application software
Artificial intelligence - Medical applications Medical informatics |
| ISBN | 981-16-3631-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- A CNN-Based Method for Depression Detecting Form Audio -- 1 Introduction -- 2 Preliminaries -- 2.1 Mel Frequency Cepstrum Coefficient -- 2.2 Network Description -- 3 Depression Detection Model Based on CNN -- 3.1 Method -- 3.2 Extraction of MFCC Features -- 3.3 Acquisition and Classification of High-Level Features -- 4 Experiments and Discussions -- 4.1 Implementation Details -- 4.2 Depression Recognition Results -- 5 Conclusion -- References -- Design of Chinese Medicine Health Management System -- 1 Introduction -- 2 Methods -- 2.1 Method of TCM BC Identification -- 2.2 Method of TCM Disease Prevention -- 2.3 Method of TCM Disease Treatment -- 2.4 Design Method of TCM Health Management System -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- Early Warning and Response of Emerging Infectious Diseases with Hospitals as the Main Body -- 1 Introduction -- 2 Relevant Provisions on Early Warning -- 2.1 Provisions of the Law on Prevention and Control of Infectious Diseases -- 2.2 Provisions of Emergency Response Law -- 2.3 Provisions of Regulations on Public Health Emergencies -- 3 Early Warning Chain of Emerging Infectious Diseases -- 3.1 Before Emerging Infectious Disease Case -- 3.2 After Emerging Infectious Disease Case -- 3.3 Emerging Infectious Disease Epidemic -- 3.4 Emerging Infectious Disease Pandemic -- 4 Grading Early Warning and Response of Major Infectious Diseases with Hospitals as the Main Body -- 5 Conclusions -- References -- Exploring Patients' AI Adoption Intention in the Context of Healthcare -- 1 Introduction -- 2 Theoretical Background -- 2.1 Artificial Intelligence in Healthcare -- 2.2 Factors Influencing AI Adoption -- 3 Model and Hypothesis Development -- 3.1 The Impact of Relative Advantage and Perceived Ease of Use.
3.2 The Impact of Perceived Risk and Fear of Technological Advance -- 3.3 The Mediation Role of Trust -- 4 Methodology -- 4.1 Sampling -- 4.2 Instrument Development -- 5 Data Analysis and Results -- 5.1 Descriptive Statistical Analysis -- 5.2 Measurement Validity and Reliability -- 5.3 Structural Equation Modeling Analysis -- 5.4 Common Method Bias -- 6 Discussion and Conclusions -- 6.1 Theoretical and Practical Implications -- 6.2 Limitations and Suggestions for Future Research -- References -- Hierarchical Staffing Problem Under High-Time Varying Demand -- 1 Introduction -- 2 Problem Definition -- 2.1 Overall Description of the Problem -- 2.2 Assumptions -- 3 Application of Two-Stage Method -- 3.1 Global Structure of the Method -- 3.2 Parameter Definition -- 3.3 Two-Stage Method -- 4 Results -- 4.1 Parameter Setting -- 4.2 Experiment Results -- 4.3 Discussions and Summaries -- 5 Conclusions -- References -- Research on the Demands of the Elderly in the Community Home-Based Care Model -- 1 Introduction -- 2 Literature Review -- 2.1 Current Services of Community Home-Based Care Model -- 2.2 Influence Factors of Elderly's Demands -- 3 Materials and Methods -- 3.1 Data and Sample -- 3.2 Variables -- 3.3 Empirical Approach -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- The Utilization of Online Medical Resources and the Influencing Factors -- 1 Introduction -- 2 Analysis of the Utilization of Online Medical Resources -- 2.1 Data Description -- 2.2 The Distribution of Online Medical Service Resources -- 2.3 The Flow of Online Medical Resources -- 3 Analysis of the Impact Factors of the Use of Online Medical Resources -- 3.1 Factors Influencing Incoming and Outgoing Consultations -- 3.2 Factors Influencing Patient Consultation in Each Province -- 4 Conclusions -- References. The Adoption of Artificial Intelligence in the E-Commerce Trade of Healthcare Industry -- 1 Introduction -- 2 Literature Review -- 2.1 Definition of AI and B2B Supply Chain -- 2.2 The Technology-Push and Need-Pull (TP-NP) Concepts -- 2.3 Technology- Organizational- Environmental (TOE) Framework -- 3 Proposition -- 3.1 TP-NP Concept Proposal -- 3.2 TOE Framework -Hypotheses -- 4 Conclusions and Future Work -- Appendix A. Scale Items -- References -- The Effectiveness of the Physician-Patient Relationship Crisis Communication Strategy -- 1 Introduction -- 2 Literature Review -- 3 Theoretical Foundation and Hypotheses Development -- 4 Empirical Analysis -- 4.1 Data Description -- 4.2 Sentiment Analysis -- 4.3 Sentiment Tendency -- 5 Conclusion -- References -- The Influence of User Perceived Value of Sports APP on Platform Commodity Purchase -- 1 Introduction -- 1.1 Research Background -- 1.2 Review of Related Literature -- 1.3 Research Significance -- 2 Theoretical Background -- 2.1 Sports APP -- 2.2 Perceived Value -- 3 Research Model -- 3.1 Ideas -- 3.2 Methods -- 3.3 Hypothesis -- 4 Methodology -- 4.1 Research Design -- 4.2 Variables -- 4.3 The Distribution and Recovery of Questionnaires -- 5 Data Analysis -- 5.1 Data Preprocessing -- 5.2 Descriptive Statistics -- 5.3 Sample Data and Validity Analysis -- 5.4 Correlation Analysis -- 5.5 Regression Analysis -- 6 Conclusions -- 7 Research Limitations -- Questionnaire: -- References -- Developing a Smart Personal Health Monitoring Architecture and Its Capacity -- 1 Introduction -- 2 Literature Review -- 2.1 An Overview of Event-Driven Architecture -- 3 Event-Driven Architecture Capacity -- 3.1 Flexibility -- 3.2 Sensing -- 3.3 Interoperability -- 3.4 Responding Capability -- 4 Discussion and Conclusion -- References. From Isolation to Coordination: How Can Telemedicine Help Combat the COVID-19 Outbreak? -- 1 Introduction -- 2 Emergency Telemedicine Consultation System for Combating COVID-19 -- 3 Performance Evaluation of Emergency Telemedicine Consultation System -- 4 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996464399203316 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Digital health and medical analytics : second international conference, DHA 2020, Beijing, China, July 25, 2020 : revised selected papers / / edited by Yichuan Wang [and four others]
| Digital health and medical analytics : second international conference, DHA 2020, Beijing, China, July 25, 2020 : revised selected papers / / edited by Yichuan Wang [and four others] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (144 pages) |
| Disciplina | 610.285 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Application software
Artificial intelligence - Medical applications Medical informatics |
| ISBN | 981-16-3631-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- A CNN-Based Method for Depression Detecting Form Audio -- 1 Introduction -- 2 Preliminaries -- 2.1 Mel Frequency Cepstrum Coefficient -- 2.2 Network Description -- 3 Depression Detection Model Based on CNN -- 3.1 Method -- 3.2 Extraction of MFCC Features -- 3.3 Acquisition and Classification of High-Level Features -- 4 Experiments and Discussions -- 4.1 Implementation Details -- 4.2 Depression Recognition Results -- 5 Conclusion -- References -- Design of Chinese Medicine Health Management System -- 1 Introduction -- 2 Methods -- 2.1 Method of TCM BC Identification -- 2.2 Method of TCM Disease Prevention -- 2.3 Method of TCM Disease Treatment -- 2.4 Design Method of TCM Health Management System -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- Early Warning and Response of Emerging Infectious Diseases with Hospitals as the Main Body -- 1 Introduction -- 2 Relevant Provisions on Early Warning -- 2.1 Provisions of the Law on Prevention and Control of Infectious Diseases -- 2.2 Provisions of Emergency Response Law -- 2.3 Provisions of Regulations on Public Health Emergencies -- 3 Early Warning Chain of Emerging Infectious Diseases -- 3.1 Before Emerging Infectious Disease Case -- 3.2 After Emerging Infectious Disease Case -- 3.3 Emerging Infectious Disease Epidemic -- 3.4 Emerging Infectious Disease Pandemic -- 4 Grading Early Warning and Response of Major Infectious Diseases with Hospitals as the Main Body -- 5 Conclusions -- References -- Exploring Patients' AI Adoption Intention in the Context of Healthcare -- 1 Introduction -- 2 Theoretical Background -- 2.1 Artificial Intelligence in Healthcare -- 2.2 Factors Influencing AI Adoption -- 3 Model and Hypothesis Development -- 3.1 The Impact of Relative Advantage and Perceived Ease of Use.
3.2 The Impact of Perceived Risk and Fear of Technological Advance -- 3.3 The Mediation Role of Trust -- 4 Methodology -- 4.1 Sampling -- 4.2 Instrument Development -- 5 Data Analysis and Results -- 5.1 Descriptive Statistical Analysis -- 5.2 Measurement Validity and Reliability -- 5.3 Structural Equation Modeling Analysis -- 5.4 Common Method Bias -- 6 Discussion and Conclusions -- 6.1 Theoretical and Practical Implications -- 6.2 Limitations and Suggestions for Future Research -- References -- Hierarchical Staffing Problem Under High-Time Varying Demand -- 1 Introduction -- 2 Problem Definition -- 2.1 Overall Description of the Problem -- 2.2 Assumptions -- 3 Application of Two-Stage Method -- 3.1 Global Structure of the Method -- 3.2 Parameter Definition -- 3.3 Two-Stage Method -- 4 Results -- 4.1 Parameter Setting -- 4.2 Experiment Results -- 4.3 Discussions and Summaries -- 5 Conclusions -- References -- Research on the Demands of the Elderly in the Community Home-Based Care Model -- 1 Introduction -- 2 Literature Review -- 2.1 Current Services of Community Home-Based Care Model -- 2.2 Influence Factors of Elderly's Demands -- 3 Materials and Methods -- 3.1 Data and Sample -- 3.2 Variables -- 3.3 Empirical Approach -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- The Utilization of Online Medical Resources and the Influencing Factors -- 1 Introduction -- 2 Analysis of the Utilization of Online Medical Resources -- 2.1 Data Description -- 2.2 The Distribution of Online Medical Service Resources -- 2.3 The Flow of Online Medical Resources -- 3 Analysis of the Impact Factors of the Use of Online Medical Resources -- 3.1 Factors Influencing Incoming and Outgoing Consultations -- 3.2 Factors Influencing Patient Consultation in Each Province -- 4 Conclusions -- References. The Adoption of Artificial Intelligence in the E-Commerce Trade of Healthcare Industry -- 1 Introduction -- 2 Literature Review -- 2.1 Definition of AI and B2B Supply Chain -- 2.2 The Technology-Push and Need-Pull (TP-NP) Concepts -- 2.3 Technology- Organizational- Environmental (TOE) Framework -- 3 Proposition -- 3.1 TP-NP Concept Proposal -- 3.2 TOE Framework -Hypotheses -- 4 Conclusions and Future Work -- Appendix A. Scale Items -- References -- The Effectiveness of the Physician-Patient Relationship Crisis Communication Strategy -- 1 Introduction -- 2 Literature Review -- 3 Theoretical Foundation and Hypotheses Development -- 4 Empirical Analysis -- 4.1 Data Description -- 4.2 Sentiment Analysis -- 4.3 Sentiment Tendency -- 5 Conclusion -- References -- The Influence of User Perceived Value of Sports APP on Platform Commodity Purchase -- 1 Introduction -- 1.1 Research Background -- 1.2 Review of Related Literature -- 1.3 Research Significance -- 2 Theoretical Background -- 2.1 Sports APP -- 2.2 Perceived Value -- 3 Research Model -- 3.1 Ideas -- 3.2 Methods -- 3.3 Hypothesis -- 4 Methodology -- 4.1 Research Design -- 4.2 Variables -- 4.3 The Distribution and Recovery of Questionnaires -- 5 Data Analysis -- 5.1 Data Preprocessing -- 5.2 Descriptive Statistics -- 5.3 Sample Data and Validity Analysis -- 5.4 Correlation Analysis -- 5.5 Regression Analysis -- 6 Conclusions -- 7 Research Limitations -- Questionnaire: -- References -- Developing a Smart Personal Health Monitoring Architecture and Its Capacity -- 1 Introduction -- 2 Literature Review -- 2.1 An Overview of Event-Driven Architecture -- 3 Event-Driven Architecture Capacity -- 3.1 Flexibility -- 3.2 Sensing -- 3.3 Interoperability -- 3.4 Responding Capability -- 4 Discussion and Conclusion -- References. From Isolation to Coordination: How Can Telemedicine Help Combat the COVID-19 Outbreak? -- 1 Introduction -- 2 Emergency Telemedicine Consultation System for Combating COVID-19 -- 3 Performance Evaluation of Emergency Telemedicine Consultation System -- 4 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910488719803321 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digital healthcare in Germany : market access for innovations / / Stefan Walzer, editor
| Digital healthcare in Germany : market access for innovations / / Stefan Walzer, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (109 pages) |
| Disciplina | 610.285 |
| Collana | Contributions to Economics |
| Soggetto topico |
Artificial intelligence - Medical applications
Medical informatics - Germany |
| ISBN |
9783030940256
9783030940249 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910568248203321 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digitalization in healthcare : implementing innovation and artificial intelligence / / Patrick Glauner, Philipp Plugmann, Guido Lerzynski, editors
| Digitalization in healthcare : implementing innovation and artificial intelligence / / Patrick Glauner, Philipp Plugmann, Guido Lerzynski, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (301 pages) |
| Disciplina | 610 |
| Collana | Future of Business and Finance |
| Soggetto topico |
Medical innovations
Artificial intelligence - Medical applications Telecommunication in medicine |
| ISBN | 3-030-65896-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- About the Book -- Contents -- About the Editors -- Artificial Intelligence in Healthcare: Foundations, Opportunities and Challenges -- 1 Introduction -- 2 Artificial Intelligence -- 2.1 Foundations -- 2.2 The Three Pillars of Machine Learning -- 2.3 Recent Developments -- 3 Applications in Healthcare -- 4 Opportunities -- 5 Challenges -- 5.1 Methodological Advances in Artificial Intelligence Research -- 5.2 Impact of Artificial Intelligence on Our Society -- 5.3 Education and the Need for Data Literacy -- 6 Conclusions -- References -- Opening the Door for Digital Transformation in Hospitals: Management's Point of View -- 1 Introduction -- 2 Digitalisation Strategies -- 3 The Development of a Digitalisation Strategy -- 4 How Can Measures Be Successfully Implemented? -- 4.1 Planning -- 4.2 Implementation and Setbacks -- 5 Defining a Digital Strategy Is Only the First Step -- 6 Interaction Between Supply Structures in the Healthcare Sector -- 7 IT and Data Security -- 8 Conclusions -- References -- Opening the Door for Digital Transformation in Hospitals: IT Expert's Point of View -- 1 Introduction -- 2 Recommended Areas of Digital Activities -- 3 Further Success Factors in Digital Transformation -- 3.1 Organizational Structure Is More Than Theory -- 3.2 Who Drives the Change? -- 3.3 You Are Not Alone -- 3.4 Never Underestimate the Power of Start-ups -- 3.5 Connect Yourself -- 3.6 Step by Step or: Be Pragmatic -- 4 Conclusion -- References -- Digitalization from the Patients' Perspective -- 1 Introduction -- 2 Three of Our Studies from Recent Years -- 2.1 Results from Study No. 1 -- 2.2 Results from Study No. 2 -- 2.2.1 Findings -- 2.2.2 Conclusions -- 2.3 Results from Study No. 3 -- 3 Conclusions -- References -- Digitalization in Rehabilitation -- 1 Introduction -- 2 Strengths and Weaknesses of the Current Offer.
3 Conclusion -- References -- Digitalization in Dentistry -- 1 Introduction -- 2 3D Printing Technology -- 3 Digital Imaging in Dentistry -- 4 Cyber Security -- 5 Apps and Smartphones -- Literature -- Changes in Medical Processes Due to Digitalization: Examples from Telemedicine -- 1 Introduction -- 2 A Brief History of Telemedicine -- 3 The Main Process of Patient Care (Patient History-Diagnosis-Therapy) -- 3.1 Telediagnostics -- 3.2 Telediagnostics with Home Care in Acute Primary Care or Teletriage -- 4 Further Processes of Patient Care (Data Collection, Data Analysis, Administration and Accounting) -- 4.1 Home Care -- 4.2 mHealth -- 4.3 Highly Specialized Fields -- 5 Challenges and Potentials in the Implementation of the Changes in the Daily Medical Routine -- 5.1 Challenges -- 5.2 Potential and Success Factors -- 6 Conclusion -- References -- COVID-19 as a Driver for Digital Transformation in Healthcare -- 1 Introduction -- 2 Telemedicine for Remote Care Delivery -- 3 AI, Big Data, and Mobile Applications in the Context of COVID-19 -- 3.1 Contact Tracing and Containment of COVID-19 -- 3.2 Diagnosis of COVID-19 Cases -- 3.3 Treatment of COVID-19 Cases -- 4 Conclusion -- References -- The Radiology of the Future -- 1 Introduction -- 2 A Retrospective: Wow the Digital Transformation of Radiology Started -- 3 The Present: Artificial Intelligence, Machine Learning, Deep Learning, and Convoluted Neural Networks -- 3.1 Computer-Assisted Diagnosis -- 3.2 Convoluted Neuronal Networks -- 4 The Radiologist's New Job Description in the Age of Digitalization -- 5 The Patient's Perspective in Radiological Digitalization -- 6 Conclusion -- References -- Digitalization of Pneumological Care in the Outpatient Sector: An Inventory -- 1 Introduction -- 2 Telematics: Telemedicine and Telenursing -- 2.1 Virtual Consultation Hours -- 2.2 Digital Training Material. 2.3 Digital Patient Training Programs -- 2.4 Telenursing -- 3 Telematics: Telemonitoring -- 3.1 Data Processing with the Therapy Device -- 3.2 Sleep-Related Respiratory Disorders and Telemonitoring -- 3.3 Real-Time Analysis and Data Protection -- 4 Digital Products: Increasing Adherence -- 4.1 Electronic Diaries -- 4.2 Apps -- 4.3 Adds-on for Inhalation Devices -- 4.4 Smart Devices/E-Devices -- 5 Challenges -- 5.1 Intersectoral Cooperation with Standardized Interfaces -- 5.2 Data Protection -- 5.3 EBM (Uniform Assessment Criteria of the Federal Association of Statutory Health Insurance Physicians) -- 6 Conclusion -- References -- Computer Vision Applications in Medical Diagnostics -- 1 Overview of Computer Vision -- 2 Computer Vision in Healthcare Diagnostics: Applications -- 2.1 Applying Computer Vision in Practice -- 2.2 Examples of Applications -- 3 Computer Vision in Healthcare Diagnostics: Opportunities and Challenges -- 4 Conclusion -- References -- Home 4.0: With Sensor Data from Everyday Life to Health and Care Prognosis -- 1 Research Project Home4.0 -- 1.1 Starting Situation -- 1.2 Relevance to Society -- 2 Research Concept -- 2.1 Bio-psycho-socio-technical Model -- 2.2 Networking in the Region and Community Care Model -- 2.3 Project Evaluation -- 3 Technology and Implementation -- 4 Conclusion -- References -- Digital Healthcare Applications: Marketing, Sales, and Communication -- 1 Introduction -- 2 Marketing for Digital Healthcare Applications -- 3 Systematic Sales Management as a Basis for Market Entry and Sales of Digital Health Applications -- 3.1 From Prospect to Customer -- 3.2 Sales Instruments in Concerted Interaction -- 3.3 Lead Generation and Qualification with Systematic Sales Management -- 3.4 Management of Prospects/Leads -- 3.5 The Method of Systematic Sales Management -- 4 Conclusion and Outlook -- References. Ethical Implications of Digitalization in Healthcare -- 1 Introduction -- 1.1 Responsibility of Patients -- 1.2 Responsibility of Institutions -- 1.3 Responsibilities of Society -- 2 Pitfalls of Digital Applications -- 3 Opportunities and Challenges -- 3.1 Opportunities -- 3.2 Challenges -- 4 Conclusion -- References -- Efficiently Delivering Healthcare by Repurposing Solution Principles from Industrial Condition Monitoring: A Meta-Analysis -- 1 Introduction -- 2 Methodology -- 3 Results -- 3.1 Proposed Use Case: Comprehensive Home Care Monitoring -- 3.2 Further Proposed Use Cases -- 4 Conclusions and Outlook -- References -- Microservices as Architectural Style -- 1 Why Microservices -- 1.1 Example: Pharmacy -- 2 Discussion of a Microservice Approach -- 2.1 Event Storming of the System -- 2.2 Domain Boundaries -- 2.3 Services and Contracts -- 2.3.1 Services -- 2.3.2 Contracts -- 2.4 Technical Architecture of the Sample -- 2.5 Enhancement of the System -- 3 Discussion of a Layered Monolith -- 4 Advantages and Disadvantages of Microservices -- References -- Value-Added Process Design for Digital Transformation in Hospitals and Medical Networks -- 1 General Thoughts -- 1.1 Clinical Data and Participations -- 1.2 The Partners and Their Particular Interests -- 1.3 Current Digital Networks in Field Test -- 1.4 Devices for Digital Data Acquisition and Documentation -- 1.5 The Patient as Customer -- 2 Practical Examples -- 3 Conclusion -- References -- Digital Pharmacy -- 1 Introduction -- 2 Patient No. 1: Self-Medication Request -- 3 Patient No. 2: Simple Request of Prescription Medicines -- 4 Patient No. 3: Complex Request of Prescription Medicines -- 5 Let Us Try to Be Naive -- 6 Just Brainstorming Here -- 6.1 Find the Right Medication/Product -- 6.2 Safety Issues -- 6.3 Consultation -- 7 Conclusion -- References -- Smart Contracts in Healthcare. 1 Introduction -- 2 Decentralized Digital Ledger Technology with Smart Contracts: A Chance for Healthcare -- 3 Privacy Laws to Secure the Patients' Data: HIPAA and GDPR -- 4 What Is Interoperability in Healthcare? -- 5 The Step Towards Patient-Centered Care as a Modern Healthcare Model -- 6 The Whole DApp Workflow Must Be GDPR or HIPAA Compliant -- 7 Future Decentralized DLT Frameworks Should Support Turing-Completeness -- 8 User Identification and Authentication Is a Key Support Prerequisite of DApps -- 9 DApps Need to Have Structural Interoperability Integration -- 10 DApps and Its DLT Framework Must Have High Scalability to Manage a High Number of Patients in the National/Global Healthcare System -- 11 How Can Decentralized Systems with Smart Contracts Be Cost-Effective in Comparison to Centralized Existing Approaches? -- 12 Support of Patient-Centered Care Model -- 13 Potential Benefits and Examples of DLT with Smart Contracts in Health Care Ecosystems -- 14 Conclusion -- References -- Evaluating the Ethical Aspects of Online Counseling -- 1 Introduction -- 2 Psychological Counseling -- 2.1 A Brief Introduction to Psychological Counseling -- 2.2 Counseling Ethics and Regulation -- 3 Digitalization in Psychological Counseling -- 4 Ethical Evaluation of Online Counseling -- 4.1 A Model for the Ethical Evaluation of Socio-technical Arrangements (MEESTAR) -- 4.2 Ethical Evaluation of Online Counseling -- 5 Conclusion -- References -- Machine Learning as Key Technology of AI: Automated Workforce Planning -- 1 Introduction -- 2 Machine Learning -- 2.1 Data Mining -- 2.2 Learning Methods -- 2.3 Deep Learning and Neural Networks -- 3 Requirements for the Use of AI Systems -- 4 Challenging Aspects -- 4.1 Resources -- 4.2 Society and Legislation -- 4.3 Data and Interfaces -- 5 Automated Workforce Planning System -- 5.1 Components -- 5.2 Advantages. 5.3 Roadmap for Implementation. |
| Record Nr. | UNINA-9910484276003321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : third MICCAI workshop, FAIR 2022, and third MICCAI workshop, DeCaF 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Shadi Albarqouni [and twelve others], editors
| Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : third MICCAI workshop, FAIR 2022, and third MICCAI workshop, DeCaF 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Shadi Albarqouni [and twelve others], editors |
| Pubbl/distr/stampa | Cham : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (215 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Artificial intelligence - Medical applications
Diagnostic imaging - Data processing Machine learning |
| ISBN | 3-031-18523-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface DeCaF 2022 -- Preface FAIR 2022 -- Organization -- Contents -- Distributed, Collaborative, and Federated Learning -- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation -- 1 Introduction -- 2 Method -- 2.1 Problem Setup -- 2.2 Preliminary -- 2.3 Proposed Incremental Transfer Learning Multi-site Method -- 3 Experiments -- 4 Analysis and Discussion -- 5 Conclusion -- References -- FedAP: Adaptive Personalization in Federated Learning for Non-IID Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Definitions -- 3.2 Federated Averaging -- 3.3 Federated Adaptive Personalization -- 3.4 Hierarchical Clustering -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Results and Discussions -- 5 Conclusion -- References -- Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes? -- 1 Introduction -- 2 Data Stealing Attack -- 2.1 Attack Strategy -- 2.2 Attack Implementation -- 3 Experiments -- 3.1 Datasets and Models -- 3.2 Effectiveness of Data Stealing Attacks -- 3.3 Mitigation of Data Stealing Attacks -- 4 Conclusion -- References -- Can Collaborative Learning Be Private, Robust and Scalable? -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Performance Overview -- 4.3 Different Privacy Regimes Under Quantization -- 4.4 Using Adversarial Training -- 4.5 Train- and Inference-Time Attacks -- 5 Discussion and Conclusion -- References -- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Split-U-Net -- 2.2 Measuring Data Leakage by Inversion Attack -- 2.3 Defenses -- 3 Experiments and Results -- 4 Discussion -- References.
Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning -- 1 Introduction -- 2 Methods -- 2.1 Federated Learning -- 2.2 Federated Averaging for Learning from Partial Labels -- 3 Experimental Details and Results -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Experimental Results -- 3.4 Validation on External Dataset -- 4 Discussion -- 5 Conclusion -- References -- GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Generative Adversarial Network -- 3.3 Privacy-Preserving Aggregation -- 4 Experiments and Results -- 4.1 Datasets and Training Procedure -- 4.2 Experimental Results -- 5 Conclusion -- References -- A Specificity-Preserving Generative Model for Federated MRI Translation -- 1 Introduction -- 2 Theory -- 2.1 MRI Translation with Adversarial Models -- 2.2 Specificity-Preserving Federated Learning of MRI Translation -- 3 Methods -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Experiments -- 4 Results -- 5 Discussion and Conclusion -- References -- Content-Aware Differential Privacy with Conditional Invertible Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 (Conditional) Invertible Neural Networks -- 3.2 Content-Aware Differential Privacy -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusion -- References -- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain -- 1 Introduction -- 2 Preliminary -- 2.1 Blockchain -- 2.2 Self-supervised Learning (SSL) -- 3 Method -- 3.1 Overview of the Framework -- 3.2 Launch Efficient Deep Learning Training on Blockchain -- 3.3 Secure Training on Blockchain with User Selection -- 4 Experiment -- 4.1 Experiment Setup and Datasets -- 4.2 Comparison Between Aggregation Methods. 4.3 Comparison Between Learning Strategies -- 5 Conclusion -- References -- Cluster Based Secure Multi-party Computation in Federated Learning for Histopathology Images -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Experimental Details -- 3.3 Results and Discussions -- 4 Conclusions -- References -- Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling-4pt -- 1 Introduction -- 2 Related Work -- 3 Background: SV Computation -- 4 Shapley Value for Federated Learning Using Ensembling -- 5 Experimental Evaluation -- 6 Results -- 7 Conclusion -- References -- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments -- 5 Results -- 6 Discussion -- References -- Towards Sparsified Federated Neuroimaging Models via Weight Pruning -- 1 Introduction -- 2 Neuroimaging Learning Environments -- 3 Model Pruning -- 4 Results -- 5 Discussion -- References -- Affordable AI and Healthcare -- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Past Work -- 1.3 Baseline Performance on p-OCT Data and Dataset Details -- 2 Super-Resolving p-OCT Data with ESRGAN -- 2.1 ESRGAN Background and Methods -- 2.2 ESRGAN Results and Discussion -- 3 Enhancing Source Domain Perceptual Image Quality with MedGAN -- 3.1 MedGAN Background -- 3.2 MedGAN Methods -- 3.3 MedGAN Results and Discussion -- 4 Conclusions and Future Directions -- References -- Deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions -- 1 Introduction -- 2 Materials -- 3 Methods -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusions -- References. Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks -- 1 Introduction -- 2 Method -- 2.1 Stand-Alone Self-attention -- 2.2 Quantisation of Network Parameters -- 2.3 Network Architecture -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 4 Results and Discussions -- 4.1 Qualitative Analysis -- 4.2 Quantitative Analysis -- 4.3 Computational Analysis -- 4.4 Analysis of Clinical Relevance -- 5 Conclusion -- References -- LRH-Net: A Multi-level Knowledge Distillation Approach for Low-Resource Heart Network -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing -- 2.2 Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results and Discussion -- 4.1 Baselines -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996495570603316 |
| Cham : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : third MICCAI workshop, FAIR 2022, and third MICCAI workshop, DeCaF 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Shadi Albarqouni [and twelve others], editors
| Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : third MICCAI workshop, FAIR 2022, and third MICCAI workshop, DeCaF 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Shadi Albarqouni [and twelve others], editors |
| Pubbl/distr/stampa | Cham : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (215 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Artificial intelligence - Medical applications
Diagnostic imaging - Data processing Machine learning |
| ISBN | 3-031-18523-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface DeCaF 2022 -- Preface FAIR 2022 -- Organization -- Contents -- Distributed, Collaborative, and Federated Learning -- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation -- 1 Introduction -- 2 Method -- 2.1 Problem Setup -- 2.2 Preliminary -- 2.3 Proposed Incremental Transfer Learning Multi-site Method -- 3 Experiments -- 4 Analysis and Discussion -- 5 Conclusion -- References -- FedAP: Adaptive Personalization in Federated Learning for Non-IID Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Definitions -- 3.2 Federated Averaging -- 3.3 Federated Adaptive Personalization -- 3.4 Hierarchical Clustering -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Results and Discussions -- 5 Conclusion -- References -- Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes? -- 1 Introduction -- 2 Data Stealing Attack -- 2.1 Attack Strategy -- 2.2 Attack Implementation -- 3 Experiments -- 3.1 Datasets and Models -- 3.2 Effectiveness of Data Stealing Attacks -- 3.3 Mitigation of Data Stealing Attacks -- 4 Conclusion -- References -- Can Collaborative Learning Be Private, Robust and Scalable? -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Performance Overview -- 4.3 Different Privacy Regimes Under Quantization -- 4.4 Using Adversarial Training -- 4.5 Train- and Inference-Time Attacks -- 5 Discussion and Conclusion -- References -- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Split-U-Net -- 2.2 Measuring Data Leakage by Inversion Attack -- 2.3 Defenses -- 3 Experiments and Results -- 4 Discussion -- References.
Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning -- 1 Introduction -- 2 Methods -- 2.1 Federated Learning -- 2.2 Federated Averaging for Learning from Partial Labels -- 3 Experimental Details and Results -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Experimental Results -- 3.4 Validation on External Dataset -- 4 Discussion -- 5 Conclusion -- References -- GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Generative Adversarial Network -- 3.3 Privacy-Preserving Aggregation -- 4 Experiments and Results -- 4.1 Datasets and Training Procedure -- 4.2 Experimental Results -- 5 Conclusion -- References -- A Specificity-Preserving Generative Model for Federated MRI Translation -- 1 Introduction -- 2 Theory -- 2.1 MRI Translation with Adversarial Models -- 2.2 Specificity-Preserving Federated Learning of MRI Translation -- 3 Methods -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Experiments -- 4 Results -- 5 Discussion and Conclusion -- References -- Content-Aware Differential Privacy with Conditional Invertible Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 (Conditional) Invertible Neural Networks -- 3.2 Content-Aware Differential Privacy -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusion -- References -- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain -- 1 Introduction -- 2 Preliminary -- 2.1 Blockchain -- 2.2 Self-supervised Learning (SSL) -- 3 Method -- 3.1 Overview of the Framework -- 3.2 Launch Efficient Deep Learning Training on Blockchain -- 3.3 Secure Training on Blockchain with User Selection -- 4 Experiment -- 4.1 Experiment Setup and Datasets -- 4.2 Comparison Between Aggregation Methods. 4.3 Comparison Between Learning Strategies -- 5 Conclusion -- References -- Cluster Based Secure Multi-party Computation in Federated Learning for Histopathology Images -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Experimental Details -- 3.3 Results and Discussions -- 4 Conclusions -- References -- Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling-4pt -- 1 Introduction -- 2 Related Work -- 3 Background: SV Computation -- 4 Shapley Value for Federated Learning Using Ensembling -- 5 Experimental Evaluation -- 6 Results -- 7 Conclusion -- References -- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments -- 5 Results -- 6 Discussion -- References -- Towards Sparsified Federated Neuroimaging Models via Weight Pruning -- 1 Introduction -- 2 Neuroimaging Learning Environments -- 3 Model Pruning -- 4 Results -- 5 Discussion -- References -- Affordable AI and Healthcare -- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Past Work -- 1.3 Baseline Performance on p-OCT Data and Dataset Details -- 2 Super-Resolving p-OCT Data with ESRGAN -- 2.1 ESRGAN Background and Methods -- 2.2 ESRGAN Results and Discussion -- 3 Enhancing Source Domain Perceptual Image Quality with MedGAN -- 3.1 MedGAN Background -- 3.2 MedGAN Methods -- 3.3 MedGAN Results and Discussion -- 4 Conclusions and Future Directions -- References -- Deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions -- 1 Introduction -- 2 Materials -- 3 Methods -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusions -- References. Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks -- 1 Introduction -- 2 Method -- 2.1 Stand-Alone Self-attention -- 2.2 Quantisation of Network Parameters -- 2.3 Network Architecture -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 4 Results and Discussions -- 4.1 Qualitative Analysis -- 4.2 Quantitative Analysis -- 4.3 Computational Analysis -- 4.4 Analysis of Clinical Relevance -- 5 Conclusion -- References -- LRH-Net: A Multi-level Knowledge Distillation Approach for Low-Resource Heart Network -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing -- 2.2 Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results and Discussion -- 4.1 Baselines -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910616395003321 |
| Cham : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
