Anant Madabhushi – författare

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.

This book constitutes the refereed joint proceedings of the 10th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th International Workshop on Clinical Image-Based Procedures, CLIP 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. The 4 full papers presented at ML-CDS 2020 and the 9 full papers presented at CLIP 2020 were carefully reviewed and selected from numerous submissions to ML-CDS and 10 submissions to CLIP. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

This book constitutes the refereed joint proceedings of the 11th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2021, held in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in October 2021.

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; Part VII: Clinical applications – abdomen; Part VIII: Clinical applications – neuroimaging;

The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023.The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty;Part III: Machine learning – explainability, bias and uncertainty; image segmentation;Part IV: Image segmentation;Part V: Computer-aided diagnosis;Part VI: Computer-aided diagnosis; computational pathology;Part VII: Clinical applications – abdomen; clinicalapplications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy;Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration."Semantic Segmentation of Surgical Hyperspectral Images Under Geometric Domain Shifts" is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.

The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections:Part I: Machine learning with limited supervision and machine learning – transfer learning;Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinicalapplications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science;Part X: Image reconstruction and image registration.

This book constitutes the proceedings from the workshops LDTM 2024, MMMI/ML4MHD 2024, and ML-CDS 2024 which were held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, in October 2024.The papers included in this book stem from the following workshops:- LDTM 2024, Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, which accepted 13 papers from 15 submissions. - MMMI/ML4MHD 2024, the 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, and the  First Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024, from which 8 papers are included from a total of 14 submissions to the workshop- ML-CDS 2024, Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, which accepted 4 papers out of 5 submissions

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017.The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images.