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

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發(fā)表于 2025-3-21 16:23:58 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Cancer Prevention Through Early Detection
副標(biāo)題First International
編輯Sharib Ali,Fons van der Sommen,Iris Kolenbrander
視頻videohttp://file.papertrans.cn/222/221181/221181.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Cancer Prevention Through Early Detection; First International  Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceedings 202
描述.This book constitutes the refereed proceedings of the first International Workshop on Cancer Prevention through Early Detection, CaPTion, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2022...?The 16 papers presented at CaPTion 2022 were carefully reviewed and selected from 21 submissions. The workshop invites researchers to submit their work in the field of medical imaging around the central theme of early cancer detection, and it strives to address the challenges that are required to be overcomed to translate computational methods to clinical practice through well designed, generalizable (robust), interpretable and clinically transferable methods..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; bioinformatics; classification methods; computer systems; computer vision; deep
版次1
doihttps://doi.org/10.1007/978-3-031-17979-2
isbn_softcover978-3-031-17978-5
isbn_ebook978-3-031-17979-2Series 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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發(fā)表于 2025-3-21 21:27:07 | 只看該作者
Cancer Prevention Through Early Detection978-3-031-17979-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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發(fā)表于 2025-3-22 02:55:12 | 只看該作者
https://doi.org/10.1007/978-3-031-17979-2artificial intelligence; bioinformatics; classification methods; computer systems; computer vision; deep
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FDE as a Base for Constructive Logic The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ’s surface is developed, and coupled with an automatic extraction of morphologi
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Frege on Dichtung and Elucidation,t such a pipeline, it is often necessary to train a classifier, which decides if a patch (for example, sized .) provides a positive cell reading or not. Following the clinical guidance, pathologists must label many such patches of both negative (N) and positive (P) cells. Then, a deep network can be
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