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Artificial intelligence in medicine : Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99, Aalborg, Denmark, June 20-24, 1999, proceedings / / Werner Horn [and four others] (Eds.)
| Artificial intelligence in medicine : Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99, Aalborg, Denmark, June 20-24, 1999, proceedings / / Werner Horn [and four others] (Eds.) |
| Edizione | [1st ed. 1999.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer, , [1999] |
| Descrizione fisica | 1 online resource (XIV, 462 p.) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence - Medical applications
Medicine - Decision making - Data processing |
| ISBN | 3-540-48720-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynote Lectures -- From Clinical Guidelines to Decision Support -- Artificial Intelligence for Building Learning Health Care Organizations -- Timing Is Everything: Temporal Reasoning and Temporal Data Maintenance in Medicine -- Machine Learning for Data Mining in Medicine -- Guidelines and Protocols -- Guidelines-Based Workflow Systems -- Enhancing Clinical Practice Guideline Compliance by Involving Physicians in the Decision Process -- Application of Therapeutic Protocols: A Tool to Manage Medical Knowledge -- Decision Support Systems, Knowledge-Based Systems, Cooperative Systems -- From Description to Decision: Towards a Decision Support Training System for MR Radiology of the Brain -- Internet-Based Decision-Support Server for Acute Abdominal Pain -- Multi-modal Reasoning in Diabetic Patient Management -- Experiences with Case-Based Reasoning Methods and Prototypes for Medical Knowledge-Based Systems -- Exploting Social Reasoning of Open Multi-agent Systems to Enahnce Cooperation in Hospitals -- Influence Diagrams for Neonatal Jaundice Management -- Electronic Drug Prescribing and Administration - Bedside Medical Decision Making -- Neonatal Ventilation Tutor (VIE-NVT), a Teaching Program for the Mechanical Ventilation of Newborn Infants -- A Life-Cycle Based Authorisation Expert Database System -- A Decision-Support System for the Identification, Staging, and Functional Evaluation of Liver Diseases (HEPASCORE) -- Model-Based Systems -- A Model-Based Approach for Learning to Identify Cardiac Arrhythmias -- A Model-Based System for Pacemaker Reprogramming -- Integrating Deep Biomedical Models into Medical Decision Support Systems: An Interval Constraint Approach -- Neural Networks, Causal Probabilistic Networks -- A Decision Theoretic Approach to Empirical Treatment of Bacteraemia Originating from the Urinary Tract -- An ECG Ischemic Detection System Based on Self-Organizing Maps and a Sigmoid Function Pre-processing Stage -- Neural Network Recognition of Otoneurological Vertigo Diseases with Comparison of Some Other Classification Methods -- A Comparison of Linear and Non-linear Classifiers for the Detection of Coronary Artery Disease in Stress-ECG -- The Case-Based Neural Network Model and Its Use in Medical Expert Systems -- Knowledge Representation -- A Medical Ontology Library That Integrates the UMLS Metathesaurus™ -- The Use of the UMLS Knowledge Sources for the Design of a Domain Specific Ontology: A Practical Experience in Blood Transfusion -- Representing Knowledge Levels in Clinical Guidelines -- Temporal Reasoning -- Intelligent Analysis of Clinical Time Series by Combining Structural Filtering and Temporal Abstractions -- Knowledge-Based Event Detection in Complex Time Series Data -- Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data -- Visualization Techniques for Time-Oriented, Skeletal Plans in Medical Therapy Planning -- Visualizing Temporal Clinical Data on the WWW -- Machine Learning -- Machine Learning in Stepwise Diagnostic Process -- Refinement of Neuro-psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning -- The Analysis of Head Injury Data Using Decision Tree Techniques -- Machine Learning for Survival Analysis: A Case Study on Recurrence of Prostate Cancer -- ICU Patient State Characterization Using Machine Learning in a Time Series Framework -- Diagnostic Rules of Increased Reliability for Critical Medical Applications -- Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit? -- A Screening Technique for Prostate Cancer by Hair Chemical Analysis and Artificial Intelligence -- Natural Language Processing -- A Conversational Model for Health Promotion on the World Wide Web -- Types of Knowledge Required to Personalise Smoking Cessation Letters -- Small Is Beautiful — Compact Semantics for Medical Language Processing -- Speech Driven Natural Language Understanding for Hands-Busy Recording of Clinical Information -- Automatic Acquisition of Morphological Knowledge for Medical Language Processing -- Image Processing and Computer Aided Design -- A Multi-agent System for MRI Brain Segmentation -- Modelling Blood Vessels of the Eye with Parametric L-Systems Using Evolutionary Algorithms -- Animating Medical and Safety Knowledge -- Active Shape Models for Customised Prosthesis Design. |
| Record Nr. | UNISA-996465325803316 |
| Berlin ; ; Heidelberg : , : Springer, , [1999] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial intelligence in medicine : Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99, Aalborg, Denmark, June 20-24, 1999, proceedings / / Werner Horn [and four others] (Eds.)
| Artificial intelligence in medicine : Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99, Aalborg, Denmark, June 20-24, 1999, proceedings / / Werner Horn [and four others] (Eds.) |
| Edizione | [1st ed. 1999.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer, , [1999] |
| Descrizione fisica | 1 online resource (XIV, 462 p.) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence - Medical applications
Medicine - Decision making - Data processing |
| ISBN | 3-540-48720-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynote Lectures -- From Clinical Guidelines to Decision Support -- Artificial Intelligence for Building Learning Health Care Organizations -- Timing Is Everything: Temporal Reasoning and Temporal Data Maintenance in Medicine -- Machine Learning for Data Mining in Medicine -- Guidelines and Protocols -- Guidelines-Based Workflow Systems -- Enhancing Clinical Practice Guideline Compliance by Involving Physicians in the Decision Process -- Application of Therapeutic Protocols: A Tool to Manage Medical Knowledge -- Decision Support Systems, Knowledge-Based Systems, Cooperative Systems -- From Description to Decision: Towards a Decision Support Training System for MR Radiology of the Brain -- Internet-Based Decision-Support Server for Acute Abdominal Pain -- Multi-modal Reasoning in Diabetic Patient Management -- Experiences with Case-Based Reasoning Methods and Prototypes for Medical Knowledge-Based Systems -- Exploting Social Reasoning of Open Multi-agent Systems to Enahnce Cooperation in Hospitals -- Influence Diagrams for Neonatal Jaundice Management -- Electronic Drug Prescribing and Administration - Bedside Medical Decision Making -- Neonatal Ventilation Tutor (VIE-NVT), a Teaching Program for the Mechanical Ventilation of Newborn Infants -- A Life-Cycle Based Authorisation Expert Database System -- A Decision-Support System for the Identification, Staging, and Functional Evaluation of Liver Diseases (HEPASCORE) -- Model-Based Systems -- A Model-Based Approach for Learning to Identify Cardiac Arrhythmias -- A Model-Based System for Pacemaker Reprogramming -- Integrating Deep Biomedical Models into Medical Decision Support Systems: An Interval Constraint Approach -- Neural Networks, Causal Probabilistic Networks -- A Decision Theoretic Approach to Empirical Treatment of Bacteraemia Originating from the Urinary Tract -- An ECG Ischemic Detection System Based on Self-Organizing Maps and a Sigmoid Function Pre-processing Stage -- Neural Network Recognition of Otoneurological Vertigo Diseases with Comparison of Some Other Classification Methods -- A Comparison of Linear and Non-linear Classifiers for the Detection of Coronary Artery Disease in Stress-ECG -- The Case-Based Neural Network Model and Its Use in Medical Expert Systems -- Knowledge Representation -- A Medical Ontology Library That Integrates the UMLS Metathesaurus™ -- The Use of the UMLS Knowledge Sources for the Design of a Domain Specific Ontology: A Practical Experience in Blood Transfusion -- Representing Knowledge Levels in Clinical Guidelines -- Temporal Reasoning -- Intelligent Analysis of Clinical Time Series by Combining Structural Filtering and Temporal Abstractions -- Knowledge-Based Event Detection in Complex Time Series Data -- Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data -- Visualization Techniques for Time-Oriented, Skeletal Plans in Medical Therapy Planning -- Visualizing Temporal Clinical Data on the WWW -- Machine Learning -- Machine Learning in Stepwise Diagnostic Process -- Refinement of Neuro-psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning -- The Analysis of Head Injury Data Using Decision Tree Techniques -- Machine Learning for Survival Analysis: A Case Study on Recurrence of Prostate Cancer -- ICU Patient State Characterization Using Machine Learning in a Time Series Framework -- Diagnostic Rules of Increased Reliability for Critical Medical Applications -- Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit? -- A Screening Technique for Prostate Cancer by Hair Chemical Analysis and Artificial Intelligence -- Natural Language Processing -- A Conversational Model for Health Promotion on the World Wide Web -- Types of Knowledge Required to Personalise Smoking Cessation Letters -- Small Is Beautiful — Compact Semantics for Medical Language Processing -- Speech Driven Natural Language Understanding for Hands-Busy Recording of Clinical Information -- Automatic Acquisition of Morphological Knowledge for Medical Language Processing -- Image Processing and Computer Aided Design -- A Multi-agent System for MRI Brain Segmentation -- Modelling Blood Vessels of the Eye with Parametric L-Systems Using Evolutionary Algorithms -- Animating Medical and Safety Knowledge -- Active Shape Models for Customised Prosthesis Design. |
| Record Nr. | UNINA-9910767569903321 |
| Berlin ; ; Heidelberg : , : Springer, , [1999] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in medicine
| Artificial intelligence in medicine |
| Pubbl/distr/stampa | Tecklenburg, West Germany, : Burgberlag, 1989- |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Artificial intelligence Artificial Intelligence Medicine Artificial Intelligence - periodicals Medicine - periodicals Intelligence artificielle en médecine Intelligence artificielle artificial intelligence |
| Soggetto genere / forma | Periodicals. |
| ISSN | 1873-2860 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | AIIM |
| Record Nr. | UNISA-996205734503316 |
| Tecklenburg, West Germany, : Burgberlag, 1989- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial intelligence in medicine
| Artificial intelligence in medicine |
| Pubbl/distr/stampa | Tecklenburg, West Germany, : Burgberlag, 1989- |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Artificial intelligence Artificial Intelligence Medicine Artificial Intelligence - periodicals Medicine - periodicals Intelligence artificielle en médecine Intelligence artificielle artificial intelligence |
| Soggetto genere / forma | Periodicals. |
| ISSN | 1873-2860 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | AIIM |
| Record Nr. | UNINA-9910147162503321 |
| Tecklenburg, West Germany, : Burgberlag, 1989- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in ophthalmology / / Andrzej Grzybowski, editor
| Artificial intelligence in ophthalmology / / Andrzej Grzybowski, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (280 pages) |
| Disciplina | 617.700285 |
| Soggetto topico |
Ophthalmology - Data processing
Artificial intelligence - Medical applications Oftalmologia Intel·ligència artificial en medicina |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-78601-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910502588803321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in PET/CT oncologic imaging / / edited by John A. Andreou, Paris A. Kosmidis, and Athanasios D. Gouliamos
| Artificial intelligence in PET/CT oncologic imaging / / edited by John A. Andreou, Paris A. Kosmidis, and Athanasios D. Gouliamos |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (156 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Tomography, Emission
Artificial intelligence - Medical applications Processament de dades Tomografia Intel·ligència artificial en medicina |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-10090-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- 1: Introduction: Artificial Intelligence (AI) Systems for Oncology -- 1.1 Introduction -- 1.2 Applications -- 1.3 Challenges -- References -- 2: Positron Emission Tomography in Bone and Soft Tissue Tumors -- 2.1 Introduction -- 2.2 Positron Emission Tomography in Sarcomas -- 2.3 Positron Emission Tomography in Gastrointestinal Stromal Tumors -- 2.4 Artificial Intelligence -- 2.5 Conclusion -- References -- 3: PET/CT in Brain Tumors: Current Artificial Intelligence Applications -- 3.1 Introduction -- 3.2 Radiopharmaceuticals -- 3.3 Radiomics in the Study of Brain Malignancies -- 3.4 Identification of Brain Tumors, Molecular Markers, Grading and Prognosis -- 3.4.1 FDG PET -- 3.4.2 MET PET -- 3.4.3 FDOPA PET -- 3.4.4 FET PET -- 3.4.5 FLT PET and Other Tracers -- 3.5 Biopsy Guiding -- 3.6 Radiation Therapy Planning -- 3.7 Treatment Monitoring -- 3.8 Role of PET/CT in Brain Metastases -- References -- 4: Artificial Intelligence in Head and Neck Cancer Patients -- 4.1 Introduction -- 4.2 Artificial Intelligence: Performing Tasks Requiring Human Intelligence -- 4.3 Artificial Intelligence in Medicine -- 4.4 Artificial Intelligence in Oncology: Head and Neck Cancer -- 4.5 Conclusions -- References -- 5: PET-CT in Lung Cancer -- References -- 6: Breast Cancer: PET/CT Imaging -- References -- 7: PET/CT in Gynecologic Cancer -- 7.1 PET/CT with [18F]FDG in Cervical Cancer -- 7.1.1 Initial Diagnosis and Prognosis -- 7.1.2 Initial Staging -- 7.1.3 Radiotherapy Planning -- 7.1.4 Restaging after Treatment -- 7.1.5 Tumor Recurrence -- 7.1.6 Conclusion -- 7.2 PET/CT with [18F]FDG in Endometrial Cancer -- 7.2.1 Initial Diagnosis and Prognosis -- 7.2.2 Initial Staging -- 7.2.3 Tumor Recurrence -- 7.2.4 Conclusion -- 7.3 PET/CT with [18F]FDG in Ovarian Cancer.
7.3.1 Initial Diagnosis: Differentiation Between Malignant and Benign Ovarian Tumors and Prognosis -- 7.3.2 Initial Staging -- 7.3.3 Radiotherapy Planning -- 7.3.4 Restaging After Treatment -- 7.3.5 Tumor Recurrence -- 7.3.6 Conclusion -- References -- 8: PET-CT Staging of Rectal Carcinoma -- 8.1 Introduction -- 8.2 Diagnosis and Initial Staging -- 8.3 Detection and Staging of Recurrent Disease -- 8.4 Monitoring Treatment Response and Planning of Radiation Therapy -- 8.5 PET/CT Radiomics in Rectal Cancer -- 8.6 Conclusions -- References -- 9: Advances in Neuroendocrine Tumor Imaging, Including PET and Artificial Intelligence (AI) -- 9.1 Introduction -- 9.2 SSTR-Based Imaging -- 9.3 Ga-68 SSTR-vs. F18-FDG -- 9.4 Theragnostics in Neuroendocrine Tumors -- 9.5 Tentative Approach to AI in PET/CT Regarding Neuroendocrine Tumors -- References -- 10: PET/CT in the Evaluation of Adrenal Gland Mass -- 10.1 Introduction -- 10.2 PET/CT in Evaluation of Adrenal Masses in Cancer and Noncancer Patients -- 10.3 PET/CT in Primary Tumors' Evaluation -- 10.4 Towards Artificial Intelligence -- 10.5 Conclusion -- References -- 11: PET/CT in Renal Cancer -- 11.1 Introduction -- 11.2 18F-FDG-PET for Renal Cancer Investigation -- 11.2.1 Renal Mass Characterization and Initial Staging -- 11.2.2 Relapse and Evaluation of Treatment Response -- 11.3 Non-FDG Radiopharmaceutical for RCC Imaging -- 11.4 Towards Artificial Intelligence -- References -- 12: PET/CT Findings in Testicular Cancer -- 12.1 Initial Staging: Early Detection of Micrometastases -- 12.2 Response to Treatment Assessment: Residual Mass Characterization -- 12.3 Seminomatous GCTs -- 12.4 Nonseminomatous Germ Cell Tumors -- References -- 13: PET/CT in Prostate Cancer -- 13.1 Introduction -- 13.2 Imaging of Prostate Cancer with PET/CT. 13.3 Artificial Intelligence in the Service of Prostate Cancer Patients -- References -- 14: The Role of 18FDG-PET/CT in Malignant Lymphomas Clinical Implications -- 14.1 Introduction -- 14.2 PET/CT in Initial Staging -- 14.2.1 Role of PET in the Initial Staging of Lymphomas -- 14.2.2 PET in the Assessment of Bone Marrow Involvement -- 14.2.2.1 Hodgkin Lymphoma -- 14.2.2.2 Diffuse Large B Cell and Primary Mediastinal Large B Cell Lymphoma [24-33] -- 14.2.2.3 Other Lymphoma Subtypes -- 14.2.3 Potential Prognostic Impact of Baseline PET Parameters -- 14.3 PET/CT in Response Assessment After Completion of Therapy -- 14.3.1 Criteria for Response Assessment and Definitions of PET Positivity -- 14.3.2 Who Should Have an EOT-PET-Based Response Assessment and When? -- 14.3.3 Clinical Data in Individual Lymphoma Subtypes -- 14.3.3.1 Hodgkin Lymphoma -- 14.3.3.2 Primary Mediastinal Large B Cell Lymphoma -- 14.3.3.3 Diffuse Large B Cell Lymphoma -- 14.3.3.4 Follicular Lymphoma -- 14.3.3.5 Mantle Cell Lymphoma -- 14.3.3.6 T Cell Lymphomas -- 14.4 Interim Response Assessment -- 14.4.1 Who Might Benefit from Interim PET-Based Early Response Assessment? -- 14.4.2 Clinical Data in Individual Lymphoma Subtypes -- 14.4.2.1 Hodgkin Lymphoma -- 14.4.3 Is It Reasonable to Modify Treatment of HL in Response to Interim PET Results? -- 14.4.3.1 Diffuse Large B Cell Lymphoma -- 14.4.3.2 Primary Mediastinal Large B Cell Lymphoma -- 14.4.3.3 T Cell Lymphomas -- 14.5 Impact of Interim and EOT-PET on Clinical Practice: Randomized Trials -- 14.5.1 Hodgkin Lymphoma -- 14.5.1.1 Radiotherapy Questions -- 14.5.2 Chemotherapy Questions -- 14.5.3 Aggressive B Cell Lymphomas -- 14.5.3.1 Radiotherapy Questions -- 14.5.3.2 Chemotherapy Questions -- 14.6 PET in the Setting of Autologous Stem Cell Transplantation (ASCT). 14.7 PET in the Era of Novel Agents -- 14.7.1 Programmed Death-1 (PD-1) Inhibitors -- 14.7.2 Chimeric Antigen Receptor (CAR) T cells -- 14.8 Artificial Intelligence in F-FDG-PET/CT Scan -- 14.9 The Role of PET/CT in the Follow-Up of Lymphomas -- 14.10 Conclusions -- References. |
| Record Nr. | UNINA-9910620200803321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in the life sciences
| Artificial intelligence in the life sciences |
| Pubbl/distr/stampa | [Amsterdam] : , : Elsevier B.V., , [2021]- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Artificial intelligence - Medical applications
Life sciences Bioinformatics Artificial intelligence Artificial Intelligence Biological Science Disciplines Intelligence artificielle en médecine Sciences de la vie Bio-informatique Intelligence artificielle artificial intelligence biological sciences |
| Soggetto genere / forma |
Periodical
Periodicals. |
| ISSN | 2667-3185 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | AI life sciences |
| Record Nr. | UNINA-9910482915303321 |
| [Amsterdam] : , : Elsevier B.V., , [2021]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in the life sciences
| Artificial intelligence in the life sciences |
| Pubbl/distr/stampa | [Amsterdam] : , : Elsevier B.V., , [2021]- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Artificial intelligence - Medical applications
Life sciences Bioinformatics Artificial intelligence Artificial Intelligence Biological Science Disciplines Intelligence artificielle en médecine Sciences de la vie Bio-informatique Intelligence artificielle artificial intelligence biological sciences |
| Soggetto genere / forma |
Periodical
Periodicals. |
| ISSN | 2667-3185 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | AI life sciences |
| Record Nr. | UNISA-996449546803316 |
| [Amsterdam] : , : Elsevier B.V., , [2021]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial intelligence over infrared images for medical applications and medical image assisted biomarker discovery : first MICCAI workshop, AIIIMA 2022, and first MICCAI workshop, MIABID 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Siva Teja Kakileti [and nine others] (editors)
| Artificial intelligence over infrared images for medical applications and medical image assisted biomarker discovery : first MICCAI workshop, AIIIMA 2022, and first MICCAI workshop, MIABID 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Siva Teja Kakileti [and nine others] (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (200 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Artificial intelligence - Medical applications
Diagnostic imaging - Data processing |
| ISBN | 3-031-19660-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface AIIIMA 2022 -- Preface MIABID 2022 -- Organization -- Contents -- Artificial Intelligence over Infrared Images for Medical Applications -- Thermal Radiomics for Improving the Interpretability of Breast Cancer Detection from Thermal Images -- 1 Introduction -- 2 Methodology -- 2.1 Thermal Radiomics -- 2.2 Classification -- 3 Experimentation and Results -- 4 Conclusions -- References -- Radiomics for Breast IR-Imaging Classification -- 1 Introduction -- 2 Breast IR Classification in the Literature -- 3 Dataset Description -- 4 Region of Interest Segmentation -- 5 Radiomic Feature Extraction -- 6 Classification Methodology -- 7 Experiments and Results -- 8 Conclusion -- References -- Early Thermographic Screening of Breast Abnormality in Women with Dense Breast by Thermal, Fractal, and Statistical Analysis -- 1 Background -- 2 Methods -- 3 Results -- 3.1 Thermal Feature-Based Analysis -- 3.2 Fractal Feature-Based Analysis -- 3.3 Statistical Feature-Based Analysis -- 4 Discussion -- 5 Conclusion and Futurescope -- References -- A Novel Thermography-Based Artificial Intelligence-Powered Solution for Screening Breast Cancer -- 1 Introduction -- 1.1 Thermography -- 1.2 Related Work -- 1.3 AI-Powered Breast Cancer Prediction Tool by AI Talos -- 2 Materials and Methods -- 2.1 Dataset Description -- 2.2 CNN Methodology -- 3 Experimental Results -- 4 Conclusion -- References -- Thermographic Toothache Screening by Artificial Intelligence -- 1 Introduction, Review and Objectives -- 2 Materials and Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Non-fever COVID-19 Detection by Infrared Imaging -- 1 Introduction -- 2 Materials and Methods -- 2.1 Infrared Camera Calibration and Precision Assessment -- 2.2 Standard Data Bank Construction (Phase 1) -- 2.3 Classification Algorithm -- 2.4 Prospective Study (Phase 2).
2.5 Statistical Analysis -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Automated Thermal Screening for COVID-19 Using Machine Learning -- 1 Introduction -- 2 Dataset -- 2.1 Thermal Surveillance Dataset -- 2.2 Augmented Surveillance Dataset -- 2.3 Lighting Dataset -- 3 Methodology -- 3.1 Image Preprocessing -- 3.2 Face Detection -- 3.3 Fever Detection -- 3.4 Mask Classification -- 4 Experiments and Results -- 4.1 Face Detection -- 4.2 Mask Classification -- 5 Conclusion -- References -- An Automated Approach for Screening COVID-19 from Thermal Images Using Convolutional Neural Network -- 1 Introduction -- 2 Dataset -- 3 Methodology -- 3.1 Overview -- 3.2 YOLOv5 as Mask Detection Module -- 3.3 Fever Detection Module -- 4 Results and Discussion -- 5 Conclusion -- References -- Infrared Technology for Vascular Abnormality in Finding of Abdominal Aortic Aneurysm -- 1 Introduction -- 1.1 Objective -- 2 Methodology -- 2.1 Model Setup -- 2.2 Boundary Conditions -- 2.3 Physical and Thermal Properties -- 3 Verification Studies for FSI Analysis -- 4 Result and Discussions -- 4.1 Transient FSI Analysis -- 5 Limitations -- 6 Conclusion -- References -- Non-invasive Thermal Imaging for Estimation of the Fecundity of Live Female Onchocerca Worms -- 1 Introduction -- 2 Dataset Description -- 2.1 Study Site and Population -- 2.2 Imaging Protocol -- 2.3 Histopathology and Ground truth -- 3 Methodology -- 3.1 Data Pre-processing -- 3.2 Feature Extraction -- 3.3 Classification -- 4 Experiments and Results -- 5 Conclusion -- References -- Medical Image Assisted Biomarker Discovery -- Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 3.1 Dataset and Implementation Details -- 3.2 Evaluating Counterfactuals and Discovered Image-Based Markers. 3.3 Counterfactual Results -- 4 Conclusions -- References -- CoRe: An Automated Pipeline for the Prediction of Liver Resection Complexity from Preoperative CT Scans -- 1 Introduction -- 2 Methods -- 2.1 Liver, Lesion, and Vessel Segmentation -- 2.2 Topological Analysis of the Liver Vasculature -- 2.3 Quantitative Imaging Biomarkers for LR Complexity Prediction -- 3 Experiments -- 3.1 Datasets and Preprocessing -- 3.2 Training, Evaluation, and Inference -- 4 Results -- 4.1 Quantitative Results -- 4.2 Qualitative Results -- 5 Discussion and Conclusion -- References -- Diffusion Tensor Imaging Biomarkers for Parkinson's Disease Symptomatology -- 1 Introduction -- 1.1 Voxel-Based Diffusion Analysis and Voxel-Based Diktiometry -- 2 Materials and Methods -- 2.1 Patient Images and Clinical Scores -- 2.2 Preprocessing -- 2.3 Convolutional Neural Network -- 2.4 Diffusion Measures, Sensitivity Maps, and Statistical Processing -- 3 Results and Discussion -- 4 Conclusion -- References -- Prediction of Immune and Stromal Cell Population Abundance from Hepatocellular Carcinoma Whole Slide Images Using Weakly Supervised Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Gene Expression Processing -- 2.3 Image Preprocessing -- 2.4 Deep Learning Models -- 2.5 Attention Map Generation and Statistical Analysis -- 2.6 Inflammatory Cell Density Map Generation -- 3 Results -- 3.1 Unsupervised Hierarchical Clustering of Samples -- 3.2 Evaluation of Deep Learning Models for the Prediction of Activation of Cell Populations -- 3.3 Interpretability and Relationships with Immunotherapy-Related Gene Signatures and with Inflammatory Cells -- 4 Discussion and Conclusion -- References -- Enhancing Local Context of Histology Features in Vision Transformers -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Conclusion -- References. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence -- 1 Introduction -- 2 Materials -- 3 Methodology -- 3.1 Cell Detection, Cell Classification and Hotspot Analysis -- 3.2 DCIS Segmentation Using GAN -- 3.3 Stromal TIL Scoring Using Artificial Intelligence -- 3.4 Statistical Analysis -- 4 Results and Discussion -- References -- Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy -- 1 Introduction -- 2 Methodology -- 2.1 Pre-training Phase -- 2.2 BRAF Classification -- 3 Experimental Setup -- 3.1 Dataset and Evaluation Metrics -- 3.2 Experimental Challenges -- 3.3 Network Training and Computational Environment -- 4 Results and Discussion -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910631089003321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence over infrared images for medical applications and medical image assisted biomarker discovery : first MICCAI workshop, AIIIMA 2022, and first MICCAI workshop, MIABID 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Siva Teja Kakileti [and nine others] (editors)
| Artificial intelligence over infrared images for medical applications and medical image assisted biomarker discovery : first MICCAI workshop, AIIIMA 2022, and first MICCAI workshop, MIABID 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Siva Teja Kakileti [and nine others] (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (200 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Artificial intelligence - Medical applications
Diagnostic imaging - Data processing |
| ISBN | 3-031-19660-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface AIIIMA 2022 -- Preface MIABID 2022 -- Organization -- Contents -- Artificial Intelligence over Infrared Images for Medical Applications -- Thermal Radiomics for Improving the Interpretability of Breast Cancer Detection from Thermal Images -- 1 Introduction -- 2 Methodology -- 2.1 Thermal Radiomics -- 2.2 Classification -- 3 Experimentation and Results -- 4 Conclusions -- References -- Radiomics for Breast IR-Imaging Classification -- 1 Introduction -- 2 Breast IR Classification in the Literature -- 3 Dataset Description -- 4 Region of Interest Segmentation -- 5 Radiomic Feature Extraction -- 6 Classification Methodology -- 7 Experiments and Results -- 8 Conclusion -- References -- Early Thermographic Screening of Breast Abnormality in Women with Dense Breast by Thermal, Fractal, and Statistical Analysis -- 1 Background -- 2 Methods -- 3 Results -- 3.1 Thermal Feature-Based Analysis -- 3.2 Fractal Feature-Based Analysis -- 3.3 Statistical Feature-Based Analysis -- 4 Discussion -- 5 Conclusion and Futurescope -- References -- A Novel Thermography-Based Artificial Intelligence-Powered Solution for Screening Breast Cancer -- 1 Introduction -- 1.1 Thermography -- 1.2 Related Work -- 1.3 AI-Powered Breast Cancer Prediction Tool by AI Talos -- 2 Materials and Methods -- 2.1 Dataset Description -- 2.2 CNN Methodology -- 3 Experimental Results -- 4 Conclusion -- References -- Thermographic Toothache Screening by Artificial Intelligence -- 1 Introduction, Review and Objectives -- 2 Materials and Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Non-fever COVID-19 Detection by Infrared Imaging -- 1 Introduction -- 2 Materials and Methods -- 2.1 Infrared Camera Calibration and Precision Assessment -- 2.2 Standard Data Bank Construction (Phase 1) -- 2.3 Classification Algorithm -- 2.4 Prospective Study (Phase 2).
2.5 Statistical Analysis -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Automated Thermal Screening for COVID-19 Using Machine Learning -- 1 Introduction -- 2 Dataset -- 2.1 Thermal Surveillance Dataset -- 2.2 Augmented Surveillance Dataset -- 2.3 Lighting Dataset -- 3 Methodology -- 3.1 Image Preprocessing -- 3.2 Face Detection -- 3.3 Fever Detection -- 3.4 Mask Classification -- 4 Experiments and Results -- 4.1 Face Detection -- 4.2 Mask Classification -- 5 Conclusion -- References -- An Automated Approach for Screening COVID-19 from Thermal Images Using Convolutional Neural Network -- 1 Introduction -- 2 Dataset -- 3 Methodology -- 3.1 Overview -- 3.2 YOLOv5 as Mask Detection Module -- 3.3 Fever Detection Module -- 4 Results and Discussion -- 5 Conclusion -- References -- Infrared Technology for Vascular Abnormality in Finding of Abdominal Aortic Aneurysm -- 1 Introduction -- 1.1 Objective -- 2 Methodology -- 2.1 Model Setup -- 2.2 Boundary Conditions -- 2.3 Physical and Thermal Properties -- 3 Verification Studies for FSI Analysis -- 4 Result and Discussions -- 4.1 Transient FSI Analysis -- 5 Limitations -- 6 Conclusion -- References -- Non-invasive Thermal Imaging for Estimation of the Fecundity of Live Female Onchocerca Worms -- 1 Introduction -- 2 Dataset Description -- 2.1 Study Site and Population -- 2.2 Imaging Protocol -- 2.3 Histopathology and Ground truth -- 3 Methodology -- 3.1 Data Pre-processing -- 3.2 Feature Extraction -- 3.3 Classification -- 4 Experiments and Results -- 5 Conclusion -- References -- Medical Image Assisted Biomarker Discovery -- Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 3.1 Dataset and Implementation Details -- 3.2 Evaluating Counterfactuals and Discovered Image-Based Markers. 3.3 Counterfactual Results -- 4 Conclusions -- References -- CoRe: An Automated Pipeline for the Prediction of Liver Resection Complexity from Preoperative CT Scans -- 1 Introduction -- 2 Methods -- 2.1 Liver, Lesion, and Vessel Segmentation -- 2.2 Topological Analysis of the Liver Vasculature -- 2.3 Quantitative Imaging Biomarkers for LR Complexity Prediction -- 3 Experiments -- 3.1 Datasets and Preprocessing -- 3.2 Training, Evaluation, and Inference -- 4 Results -- 4.1 Quantitative Results -- 4.2 Qualitative Results -- 5 Discussion and Conclusion -- References -- Diffusion Tensor Imaging Biomarkers for Parkinson's Disease Symptomatology -- 1 Introduction -- 1.1 Voxel-Based Diffusion Analysis and Voxel-Based Diktiometry -- 2 Materials and Methods -- 2.1 Patient Images and Clinical Scores -- 2.2 Preprocessing -- 2.3 Convolutional Neural Network -- 2.4 Diffusion Measures, Sensitivity Maps, and Statistical Processing -- 3 Results and Discussion -- 4 Conclusion -- References -- Prediction of Immune and Stromal Cell Population Abundance from Hepatocellular Carcinoma Whole Slide Images Using Weakly Supervised Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Gene Expression Processing -- 2.3 Image Preprocessing -- 2.4 Deep Learning Models -- 2.5 Attention Map Generation and Statistical Analysis -- 2.6 Inflammatory Cell Density Map Generation -- 3 Results -- 3.1 Unsupervised Hierarchical Clustering of Samples -- 3.2 Evaluation of Deep Learning Models for the Prediction of Activation of Cell Populations -- 3.3 Interpretability and Relationships with Immunotherapy-Related Gene Signatures and with Inflammatory Cells -- 4 Discussion and Conclusion -- References -- Enhancing Local Context of Histology Features in Vision Transformers -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Conclusion -- References. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence -- 1 Introduction -- 2 Materials -- 3 Methodology -- 3.1 Cell Detection, Cell Classification and Hotspot Analysis -- 3.2 DCIS Segmentation Using GAN -- 3.3 Stromal TIL Scoring Using Artificial Intelligence -- 3.4 Statistical Analysis -- 4 Results and Discussion -- References -- Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy -- 1 Introduction -- 2 Methodology -- 2.1 Pre-training Phase -- 2.2 BRAF Classification -- 3 Experimental Setup -- 3.1 Dataset and Evaluation Metrics -- 3.2 Experimental Challenges -- 3.3 Network Training and Computational Environment -- 4 Results and Discussion -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996500063103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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