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Biblioteca

MARC Lista (tabellare)
Explainable Machine Learning for Multimedia Based Healthcare Applications / / M. Shamim Hossain, Utku Kose, and Deepak Gupta, editors
Explainable Machine Learning for Multimedia Based Healthcare Applications / / M. Shamim Hossain, Utku Kose, and Deepak Gupta, editors
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (240 pages)
Disciplina 610.285
Soggetto topico Artificial intelligence - Medical applications
ISBN 3-031-38036-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword -- Preface -- Acknowledgement -- Table of Contents -- Chapter 1: -- Automatic Fetal Motion Detection from Trajectory of US Videos Based on YOLOv5 and LSTM -- Chapter 2: -- Explainable Machine Learning (XML) for Multimedia-based Healthcare Systems: Opportunities, Challenges, Ethical and Future Prospects -- Chapter 3: -- Ensemble deep learning architectures in bone cancer detection based on Medical Diagnosis in Explainable Artificial Intelligence -- Chapter 4: -- Digital dermatitis disease classification utilizing visual feature extraction and various machine learning techniques by explainable AI -- Chapter 5: -- Explainable Machine Learning in Healthcare -- Chapter 6: -- Explainable Artificial Intelligence with Scaling Techniques to Classify Breast Cancer Images -- Chapter 7: -- A Novel Approach of COVID -19 Estimation Using GIS and Kmeans Clustering: A Case of GEOAI -- Chapter 8: -- A Brief Review of Explainable Artificial Intelligence Reviews and Methods -- Chapter 9: -- Systematic Literature Review In Using Big Data Analytics And XAI Applications In Medical -- Chapter 10: -- Using Explainable Artificial Intelligence In Drug Discovery: A Theoretical Research -- Chapter 11: -- Application of Interpretable Artificial Intelligence enabled Cognitive Internet of Things for COVID-19 Pandemics -- Chapter 12: -- Remote Photoplethysmography: Digital Disruption in Health Vital Acquisition.
Record Nr. UNINA-9910746085003321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exscalate4CoV : High-Performance Computing for COVID Drug Discovery / / Silvano Coletti and Gabriella Bernardi, editors
Exscalate4CoV : High-Performance Computing for COVID Drug Discovery / / Silvano Coletti and Gabriella Bernardi, editors
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023]
Descrizione fisica 1 online resource (96 pages)
Disciplina 610.285
Collana SpringerBriefs in Applied Sciences and Technology Series
Soggetto topico Artificial intelligence - Medical applications
High performance computing
ISBN 9783031306914
9783031306907
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface (Arieh Warshel) -- Acknowledgments -- How should this text be read -- Chapter 1 Introduction (Silvano Coletti, Marcello Allegretti) -- Chapter 2 A European Drug Discovery Platform: From In Silico to Experimental Validation (Gianluca Palermo, Daniela Iaconis, Philip Gribbon) -- Chapter 3 The Drug Repurposing Strategy in the Exscalate4CoV Project: Raloxifene Clinical Trials (Andrea Beccari, Lamberto Dionigi, Emanuele Nicastri, Candida Manelfi, Elizabeth Gavioli) -- Chapter 4 The High-Performance Computing Resources for the Exscalate4CoV Project (Andrew Emerson, Federico Ficarelli, Gianluca Palermo, Freancesco Frigerio) -- Chapter 5 The Impact of the Scientific Metaverse on the Biotech Industry: How Virtual Reality Helped Researchers Fight Back Against COVID-19 (Carmine Talarico, Edgardo Leija) -- Chapter 6 From Genome to Variant Interpretation Through 3D Protein Structures (Janani Durairaj, Leila Tamara Alexander, Gabriel Studer, Gerardo Tauriello, Ingrid Guarnetti Prandi, Rosalba Lepore, Giovanni Chillemi, Torsten Schwede) -- Chapter 7 The Role of Structural Biology Task Force: Validation of the Binding Mode of Repurposed Drugs Against SARS-CoV-2 Protein Targets (Paola Storici, Elisa Costanzi, Stefano Morasso) -- Chapter 8 Drug Discovery and Big Data: From Research to the Community (Luca Barbanotti, Marta Cicchetti, Gaetano Varriale) -- Chapter 9 Exploiting Drug Discovery Research for Educational Purposes (Giuliana Catara, Cristina Rigutto) -- Chapter 10 Beyond the Exscalate4CoV Project: LIGATE and REMEDI4ALL Projects (Carmine Talarico, Davide Graziani, Andrea R. Beccari) -- Conclusion (Thomas Skordas).
Record Nr. UNINA-9910720068003321
Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The fusion of internet of things, artificial intelligence, and cloud computing in health care / / Patrick Siarry [and four others], editors
The fusion of internet of things, artificial intelligence, and cloud computing in health care / / Patrick Siarry [and four others], editors
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (VIII, 268 p. 180 illus., 115 illus. in color.)
Disciplina 610.28563
Collana Internet of Things, Technology, Communications and Computing
Soggetto topico Artificial intelligence - Medical applications
Cloud computing
ISBN 3-030-75220-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Remote Patient Monitoring Using IoT, Cloud Computing and AI -- The Internet of M-Health Things (m-IoT) -- Healthcare Data Storage Options Using Cloud -- Cloud-based telemedicine ecosystem and adoption of AI -- The benefits and risks of Integrating IoT, AI in cloud services for healthcare -- Pattern Imaging Analytics using Artificial Intelligence techniques -- Identifying Diseases and Diagnosis using Artificial Intelligence -- Robotic Surgery -- Personalized Treatment with the help of IoT Artificial Intelligence and cloud -- Predicting Epidemic Outbreaks using IoT Artificial Intelligence and Cloud -- Crowd sourced Data Collection -- Maintaining Healthcare Records using Cloud storage -- Privacy and Security issues in health care based IoT -- IoT Healthcare Applications -- Applications of AI, IoT and Cloud computing in battling COVID-19 -- Intelligent Health Informatics for Handling the COVID-19 Situation -- Conclusion.
Record Nr. UNINA-9910495219203321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The fusion of internet of things, artificial intelligence, and cloud computing in health care / / Patrick Siarry [and four others], editors
The fusion of internet of things, artificial intelligence, and cloud computing in health care / / Patrick Siarry [and four others], editors
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (VIII, 268 p. 180 illus., 115 illus. in color.)
Disciplina 610.28563
Collana Internet of Things, Technology, Communications and Computing
Soggetto topico Artificial intelligence - Medical applications
Cloud computing
ISBN 3-030-75220-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Remote Patient Monitoring Using IoT, Cloud Computing and AI -- The Internet of M-Health Things (m-IoT) -- Healthcare Data Storage Options Using Cloud -- Cloud-based telemedicine ecosystem and adoption of AI -- The benefits and risks of Integrating IoT, AI in cloud services for healthcare -- Pattern Imaging Analytics using Artificial Intelligence techniques -- Identifying Diseases and Diagnosis using Artificial Intelligence -- Robotic Surgery -- Personalized Treatment with the help of IoT Artificial Intelligence and cloud -- Predicting Epidemic Outbreaks using IoT Artificial Intelligence and Cloud -- Crowd sourced Data Collection -- Maintaining Healthcare Records using Cloud storage -- Privacy and Security issues in health care based IoT -- IoT Healthcare Applications -- Applications of AI, IoT and Cloud computing in battling COVID-19 -- Intelligent Health Informatics for Handling the COVID-19 Situation -- Conclusion.
Record Nr. UNISA-996464434803316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Geospatial data science in healthcare for society 5.0 / / Pradeep K. Garg [and three others], editors
Geospatial data science in healthcare for society 5.0 / / Pradeep K. Garg [and three others], editors
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (321 pages)
Disciplina 610.28563
Collana Disruptive Technologies and Digital Transformations for Society 5. 0
Soggetto topico Artificial intelligence - Medical applications
Medical geography
ISBN 981-16-9476-1
981-16-9475-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910743346203321
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of artificial intelligence in healthcare . Volume 1 Advances and applications / / Chee-Ping Lim [and four others] editors
Handbook of artificial intelligence in healthcare . Volume 1 Advances and applications / / Chee-Ping Lim [and four others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (463 pages)
Disciplina 610.285
Collana Intelligent Systems Reference Library
Soggetto topico Artificial intelligence - Medical applications
ISBN 3-030-79161-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910523797503321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook on intelligent healthcare analytics : knowledge engineering with big data / / edited by A. Jaya [and three others]
Handbook on intelligent healthcare analytics : knowledge engineering with big data / / edited by A. Jaya [and three others]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022]
Descrizione fisica 1 online resource (441 pages)
Disciplina 610.28563
Collana Machine Learning in Biomedical Science and Healthcare Informatics Ser.
Soggetto topico Artificial intelligence - Medical applications
Medical informatics
ISBN 1-119-79255-X
1-119-79254-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910573099803321
Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook on intelligent healthcare analytics : knowledge engineering with big data / / edited by A. Jaya [and three others]
Handbook on intelligent healthcare analytics : knowledge engineering with big data / / edited by A. Jaya [and three others]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022]
Descrizione fisica 1 online resource (441 pages)
Disciplina 610.28563
Collana Machine Learning in Biomedical Science and Healthcare Informatics
Soggetto topico Artificial intelligence - Medical applications
Medical informatics
ISBN 1-119-79255-X
1-119-79254-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830877603321
Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Head and neck tumor segmentation : Second Challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Vincent Andrearczyk [and three others]
Head and neck tumor segmentation : Second Challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Vincent Andrearczyk [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (339 pages)
Disciplina 616.0754
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence - Medical applications
Diagnostic imaging - Data processing
ISBN 3-030-98253-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images -- 1 Introduction: Research Context -- 2 Dataset -- 2.1 Mission of the Challenge -- 2.2 Challenge Dataset -- 3 Task 1: Segmentation -- 3.1 Methods: Reporting of Challenge Design -- 3.2 Results: Reporting of Segmentation Task Outcome -- 4 Tasks 2 and 3: Outcome Prediction -- 4.1 Methods: Reporting of Challenge Design -- 4.2 Results: Reporting of Challenge Outcome -- 5 Discussion: Putting the Results into Context -- 5.1 Outcomes and Findings -- 5.2 Limitations of the Challenge -- 6 Conclusions -- 1 Challenge Information -- 2 Image Acquisition Details -- References -- CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing -- 2.2 Network Architecture -- 2.3 Transformer Block -- 2.4 Pixel-Wise Global Context Channel Attention -- 2.5 Squeeze and Excitation Normalization -- 2.6 Loss Function -- 3 Experiments -- 3.1 Dataset -- 3.2 Experiment Settings -- 3.3 Results -- 4 Conclusion -- References -- A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Coarse-to-Fine Framework -- 2.3 Training Details -- 3 Experiments -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Implementation Detail -- 4 Results and Discussion -- References -- Automatic Segmentation of Head and Neck (H& -- N) Primary Tumors in PET and CT Images Using 3D-Inception-ResNet Model -- 1 Introduction -- 2 Material and Methods -- 2.1 Head and Neck Tumor 2021(HECKTOR2021) Dataset Descriptions -- 2.2 Proposed Method -- 2.3 Training and Optimization Parameters -- 3 Results and Discussion -- 3.1 Quantitative Results.
3.2 Qualitative Results -- 4 Conclusion and Future Work -- References -- The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing -- 2.2 MCA Network -- 2.3 Network Architecture -- 2.4 Loss Function -- 2.5 Ensembling -- 3 Results -- 4 Discussion and Conclusion -- References -- Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Network Architecture -- 2.4 Training Scheme -- 3 Results -- 4 Discussion and Conclusions -- References -- PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 PET-Clip and PET-Sin Normalization -- 2.3 Convolutional Neural Network (CNN) -- 2.4 Experiments -- 3 Results -- 4 Discussion and Conclusion -- References -- The Head and Neck Tumor Segmentation Based on 3D U-Net -- 1 Introduction -- 2 Data and Methods -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Network Structure -- 2.4 Implementation Details -- 3 Results -- 4 Discussions -- 5 Conclusions -- References -- 3D U-Net Applied to Simple Attention Module for Head and Neck Tumor Segmentation in PET and CT Images -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 SimAM -- 2.3 Network Architecture -- 2.4 Data Preprocessing -- 2.5 Training Scheme -- 2.6 Ensembling -- 3 Result and Discussion -- 4 Conclusion -- References -- Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images -- 1 Introduction -- 2 Data and Methods -- 2.1 Dataset Description -- 2.2 Methods: An Overview -- 2.3 Co-learning Method with Multi-modal PET-CT -- 2.4 3D Skip-ScSE Multi-scale Attention Model.
3 Results -- 4 Discussion and Conclusion -- References -- Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images -- 1 Introduction -- 2 Methods -- 2.1 Imaging Data -- 2.2 Image Processing -- 2.3 Model Architecture -- 2.4 Model Implementation -- 2.5 Model Validation -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation -- 1 Introduction -- 2 Method -- 2.1 Attention Model -- 2.2 Construction of Unseen Domain -- 2.3 Model Ensemble -- 3 Experiments and Results -- 3.1 Data Preprocessing -- 3.2 Network Training -- 3.3 Experimental Results -- 4 Conclusion -- References -- Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model -- 1 Introduction -- 2 Methods -- 2.1 Image Pre-processing -- 2.2 Patch Extraction -- 2.3 Network Structure and Training -- 2.4 Prediction -- 2.5 Image Post-processing -- 2.6 Survival Prediction -- 3 Results -- 3.1 Head and Neck Tumor Segmentation -- 3.2 Survival Prediction -- 4 Discussion and Conclusions -- References -- Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients -- 1 Introduction -- 2 Materials and Methods -- 2.1 Preprocessing -- 2.2 Segmentation -- 2.3 Survival Prediction -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer -- 1 Introduction -- 2 Materials and Methods -- 2.1 Patients -- 2.2 Multi-task Deep Model -- 2.3 Loss Function -- 2.4 Data Preprocessing -- 2.5 Training Procedure -- 2.6 Ensemble -- 2.7 Evaluation Metrics -- 3 Results and Discussion -- References.
PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Machine Learning Techniques -- 1 Introduction -- 2 Task 1: Tumor Segmentation -- 2.1 Materials and Methods -- 2.2 Results -- 3 Task 2: Progression Free Survival (PFS) -- 3.1 Materials and Methods -- 3.2 Results -- 4 Conclusions -- References -- Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT Images -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Overall Network Structure -- 3.2 Encoding Pathway -- 3.3 Decoding Pathway -- 3.4 Scale Attention Block -- 3.5 Implementation -- 3.6 PFS Prediction -- 4 Results -- 5 Summary -- References -- Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival Using a Full-Scale UNet with Attention -- 1 Introduction -- 2 Methods and Data -- 2.1 Data -- 2.2 Models Description -- 2.3 Evaluation Metrics -- 3 Results -- 3.1 Segmentation Task -- 3.2 Survival Task -- 4 Conclusion -- References -- Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer -- 1 Introduction -- 2 Material and Method -- 2.1 Data Collection -- 2.2 Analysis Procedure -- 3 Results -- 3.1 Segmentation Results -- 3.2 Survival Prediction Results -- 4 Discussion -- 5 Conclusion -- 6 Code Availability -- References -- Fusion-Based Head and Neck Tumor Segmentation and Survival Prediction Using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset, PET/CT Acquisition -- 2.2 Survival Prediction -- 3 Results -- 3.1 Segmentation Task -- 3.2 Progression Free Survival Prediction Task -- 4 Conclusion -- 5 Code Availability -- References -- Head and Neck Primary Tumor Segmentation Using Deep Neural Networks and Adaptive Ensembling -- 1 Introduction -- 2 Materials and Methods.
2.1 Dataset Description -- 2.2 Data Preprocessing -- 2.3 Task 1. Modified NnUNET Model -- 2.4 Tasks 2 & -- 3. Survival Prediction -- 3 Results -- 3.1 Task 1 Segmentation Results -- 3.2 Tasks 2 & -- 3 Progression Prediction Results -- 4 Discussion -- 4.1 Task 1 -- 4.2 Task 2 & -- 3 -- 5 Conclusion -- References -- Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset -- 2.2 Proposed Methods -- 3 Results -- 3.1 Segmentation Results -- 3.2 Survival Analysis -- 4 Discussion -- 5 Conclusion -- References -- Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Tumor Segmentation -- 2.3 Survival Analysis -- 2.4 Experimental Settings -- 3 Results -- 3.1 Task 1 - Segmentation -- 3.2 Task 2 - Survival Prediction -- 4 Discussion -- 5 Conclusion -- References -- Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients -- 1 Introduction -- 2 Methods -- 2.1 Deep Supervoxel Segmentation -- 2.2 Patient Risk Score Regression -- 3 Experimental Setup -- 3.1 Data Splitting -- 3.2 Preprocessing -- 3.3 Training -- 3.4 Inference -- 4 Results -- 4.1 Segmentation Results -- 4.2 Patient Risk Score Results -- 5 Discussion and Conclusion -- References -- A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers -- 1 Introduction -- 2 Preliminary Considerations -- 2.1 Dataset Description -- 2.2 Cross-Validation Strategy -- 2.3 Evaluation of the Model -- 3 Methods -- 3.1 Feature Engineering -- 3.2 Modeling Approach -- 4 Results for Task 3 -- 4.1 Feature Selection -- 4.2 Submitted Models -- 5 Results for Task 2 -- 6 Discussion -- References.
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data.
Record Nr. UNISA-996464453703316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Head and neck tumor segmentation : Second Challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Vincent Andrearczyk [and three others]
Head and neck tumor segmentation : Second Challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Vincent Andrearczyk [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (339 pages)
Disciplina 616.0754
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence - Medical applications
Diagnostic imaging - Data processing
ISBN 3-030-98253-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images -- 1 Introduction: Research Context -- 2 Dataset -- 2.1 Mission of the Challenge -- 2.2 Challenge Dataset -- 3 Task 1: Segmentation -- 3.1 Methods: Reporting of Challenge Design -- 3.2 Results: Reporting of Segmentation Task Outcome -- 4 Tasks 2 and 3: Outcome Prediction -- 4.1 Methods: Reporting of Challenge Design -- 4.2 Results: Reporting of Challenge Outcome -- 5 Discussion: Putting the Results into Context -- 5.1 Outcomes and Findings -- 5.2 Limitations of the Challenge -- 6 Conclusions -- 1 Challenge Information -- 2 Image Acquisition Details -- References -- CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing -- 2.2 Network Architecture -- 2.3 Transformer Block -- 2.4 Pixel-Wise Global Context Channel Attention -- 2.5 Squeeze and Excitation Normalization -- 2.6 Loss Function -- 3 Experiments -- 3.1 Dataset -- 3.2 Experiment Settings -- 3.3 Results -- 4 Conclusion -- References -- A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Coarse-to-Fine Framework -- 2.3 Training Details -- 3 Experiments -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Implementation Detail -- 4 Results and Discussion -- References -- Automatic Segmentation of Head and Neck (H& -- N) Primary Tumors in PET and CT Images Using 3D-Inception-ResNet Model -- 1 Introduction -- 2 Material and Methods -- 2.1 Head and Neck Tumor 2021(HECKTOR2021) Dataset Descriptions -- 2.2 Proposed Method -- 2.3 Training and Optimization Parameters -- 3 Results and Discussion -- 3.1 Quantitative Results.
3.2 Qualitative Results -- 4 Conclusion and Future Work -- References -- The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing -- 2.2 MCA Network -- 2.3 Network Architecture -- 2.4 Loss Function -- 2.5 Ensembling -- 3 Results -- 4 Discussion and Conclusion -- References -- Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Network Architecture -- 2.4 Training Scheme -- 3 Results -- 4 Discussion and Conclusions -- References -- PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 PET-Clip and PET-Sin Normalization -- 2.3 Convolutional Neural Network (CNN) -- 2.4 Experiments -- 3 Results -- 4 Discussion and Conclusion -- References -- The Head and Neck Tumor Segmentation Based on 3D U-Net -- 1 Introduction -- 2 Data and Methods -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Network Structure -- 2.4 Implementation Details -- 3 Results -- 4 Discussions -- 5 Conclusions -- References -- 3D U-Net Applied to Simple Attention Module for Head and Neck Tumor Segmentation in PET and CT Images -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 SimAM -- 2.3 Network Architecture -- 2.4 Data Preprocessing -- 2.5 Training Scheme -- 2.6 Ensembling -- 3 Result and Discussion -- 4 Conclusion -- References -- Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images -- 1 Introduction -- 2 Data and Methods -- 2.1 Dataset Description -- 2.2 Methods: An Overview -- 2.3 Co-learning Method with Multi-modal PET-CT -- 2.4 3D Skip-ScSE Multi-scale Attention Model.
3 Results -- 4 Discussion and Conclusion -- References -- Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images -- 1 Introduction -- 2 Methods -- 2.1 Imaging Data -- 2.2 Image Processing -- 2.3 Model Architecture -- 2.4 Model Implementation -- 2.5 Model Validation -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation -- 1 Introduction -- 2 Method -- 2.1 Attention Model -- 2.2 Construction of Unseen Domain -- 2.3 Model Ensemble -- 3 Experiments and Results -- 3.1 Data Preprocessing -- 3.2 Network Training -- 3.3 Experimental Results -- 4 Conclusion -- References -- Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model -- 1 Introduction -- 2 Methods -- 2.1 Image Pre-processing -- 2.2 Patch Extraction -- 2.3 Network Structure and Training -- 2.4 Prediction -- 2.5 Image Post-processing -- 2.6 Survival Prediction -- 3 Results -- 3.1 Head and Neck Tumor Segmentation -- 3.2 Survival Prediction -- 4 Discussion and Conclusions -- References -- Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients -- 1 Introduction -- 2 Materials and Methods -- 2.1 Preprocessing -- 2.2 Segmentation -- 2.3 Survival Prediction -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer -- 1 Introduction -- 2 Materials and Methods -- 2.1 Patients -- 2.2 Multi-task Deep Model -- 2.3 Loss Function -- 2.4 Data Preprocessing -- 2.5 Training Procedure -- 2.6 Ensemble -- 2.7 Evaluation Metrics -- 3 Results and Discussion -- References.
PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Machine Learning Techniques -- 1 Introduction -- 2 Task 1: Tumor Segmentation -- 2.1 Materials and Methods -- 2.2 Results -- 3 Task 2: Progression Free Survival (PFS) -- 3.1 Materials and Methods -- 3.2 Results -- 4 Conclusions -- References -- Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT Images -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Overall Network Structure -- 3.2 Encoding Pathway -- 3.3 Decoding Pathway -- 3.4 Scale Attention Block -- 3.5 Implementation -- 3.6 PFS Prediction -- 4 Results -- 5 Summary -- References -- Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival Using a Full-Scale UNet with Attention -- 1 Introduction -- 2 Methods and Data -- 2.1 Data -- 2.2 Models Description -- 2.3 Evaluation Metrics -- 3 Results -- 3.1 Segmentation Task -- 3.2 Survival Task -- 4 Conclusion -- References -- Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer -- 1 Introduction -- 2 Material and Method -- 2.1 Data Collection -- 2.2 Analysis Procedure -- 3 Results -- 3.1 Segmentation Results -- 3.2 Survival Prediction Results -- 4 Discussion -- 5 Conclusion -- 6 Code Availability -- References -- Fusion-Based Head and Neck Tumor Segmentation and Survival Prediction Using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset, PET/CT Acquisition -- 2.2 Survival Prediction -- 3 Results -- 3.1 Segmentation Task -- 3.2 Progression Free Survival Prediction Task -- 4 Conclusion -- 5 Code Availability -- References -- Head and Neck Primary Tumor Segmentation Using Deep Neural Networks and Adaptive Ensembling -- 1 Introduction -- 2 Materials and Methods.
2.1 Dataset Description -- 2.2 Data Preprocessing -- 2.3 Task 1. Modified NnUNET Model -- 2.4 Tasks 2 & -- 3. Survival Prediction -- 3 Results -- 3.1 Task 1 Segmentation Results -- 3.2 Tasks 2 & -- 3 Progression Prediction Results -- 4 Discussion -- 4.1 Task 1 -- 4.2 Task 2 & -- 3 -- 5 Conclusion -- References -- Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset -- 2.2 Proposed Methods -- 3 Results -- 3.1 Segmentation Results -- 3.2 Survival Analysis -- 4 Discussion -- 5 Conclusion -- References -- Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Tumor Segmentation -- 2.3 Survival Analysis -- 2.4 Experimental Settings -- 3 Results -- 3.1 Task 1 - Segmentation -- 3.2 Task 2 - Survival Prediction -- 4 Discussion -- 5 Conclusion -- References -- Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients -- 1 Introduction -- 2 Methods -- 2.1 Deep Supervoxel Segmentation -- 2.2 Patient Risk Score Regression -- 3 Experimental Setup -- 3.1 Data Splitting -- 3.2 Preprocessing -- 3.3 Training -- 3.4 Inference -- 4 Results -- 4.1 Segmentation Results -- 4.2 Patient Risk Score Results -- 5 Discussion and Conclusion -- References -- A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers -- 1 Introduction -- 2 Preliminary Considerations -- 2.1 Dataset Description -- 2.2 Cross-Validation Strategy -- 2.3 Evaluation of the Model -- 3 Methods -- 3.1 Feature Engineering -- 3.2 Modeling Approach -- 4 Results for Task 3 -- 4.1 Feature Selection -- 4.2 Submitted Models -- 5 Results for Task 2 -- 6 Discussion -- References.
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data.
Record Nr. UNINA-9910552744303321
Cham, Switzerland : , : Springer, , [2022]
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