Titlebook: Data Engineering in Medical Imaging; First MICCAI Worksho Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2023 The Editor

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,Efficient Large Scale Medical Image Dataset Preparation for?Machine Learning Applications,e effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be fa

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,Vision Transformer-Based Self-supervised Learning for?Ulcerative Colitis Grading in?Colonoscopy,ed to stratify patients at higher risk of developing colorectal cancer, the phenotypic endoscopic features involved in the scoring are highly inconsistent. Thus, devising automated methods is required. However, bias in the labels can also trigger such inconsistency and inaccuracy, which makes the us

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,Task-Guided Domain Gap Reduction for?Monocular Depth Prediction in?Endoscopy, improve the quality and availability of colonoscopies by automatizing sub-tasks. One such task is predicting depth from monocular video frames, which can assist endoscopic navigation. As ground truth depth from standard in-vivo colonoscopy remains unobtainable due to hardware constraints, two appro

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/262794.jpg

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https://doi.org/10.1007/978-3-031-44992-5Informatics; Medical Imaging; Data and Label Augmentation; Active Learning; Active Synthesis; Federated L

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978-3-031-44991-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl