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Titlebook: Cancer Prevention Through Early Detection; Second International Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceedings 202

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發(fā)表于 2025-3-21 19:40:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Cancer Prevention Through Early Detection
副標(biāo)題Second International
編輯Sharib Ali,Fons van der Sommen,Iris Kolenbrander
視頻videohttp://file.papertrans.cn/222/221182/221182.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Cancer Prevention Through Early Detection; Second International Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceedings 202
描述This book constitutes the refereed proceedings of the second International Workshop on Cancer Prevention through Early Detection, CaPTion, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023..The 11 papers presented at CaPTion 2023 were carefully reviewed and selected from 12 submissions. The workshop invites researchers to submit their work in the field of medical image analysis around the central theme of cancer and early cancer detection, progression, inflammation understanding, multimodality data, and computer-aided navigation..
出版日期Conference proceedings 2023
關(guān)鍵詞medical image analysis; machine learning; deep learning; lesion classification; lesion detection; lesion
版次1
doihttps://doi.org/10.1007/978-3-031-45350-2
isbn_softcover978-3-031-45349-6
isbn_ebook978-3-031-45350-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Image Captioning for?Automated Grading and?Understanding of?Ulcerative Colitis severity of UC is done by using a widely accepted scoring system known as the “Mayo Endoscopic Scoring” (MES). The MES score is largely based on the recognition of phenotypic features of the mucosal wall, and thus the subjectivity in clinical scoring is unavoidable. An automated grading and charact
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Assessing the?Performance of?Deep Learning-Based Models for?Prostate Cancer Segmentation Using Uncer The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Net-based architectures, augmented with Monte-Carlo dropout, are evaluated for automatic segmentation of the central zone, peripheral zone, transition zone, and tumor, with uncertainty estimation. The t
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Colonoscopy Coverage Revisited: Identifying Scanning Gaps in?Real-Time Recent studies show that around 25% of the existing polyps are routinely missed. While some of these do appear in the endoscopist’s field of view, others are missed due to a partial coverage of the colon. The task of detecting and marking unseen regions of the colon has been addressed in recent wor
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ColNav: Real-Time Colon Navigation for?Colonoscopysions and provides the ability to remove them during the procedure itself. Nevertheless, failure by the endoscopist to identify such lesions increases the likelihood of lesion progression to subsequent colorectal cancer. Ultimately, colonoscopy remains operator-dependent, and the wide range of quali
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