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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

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书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
副标题25th International C
编辑Linwei Wang,Qi Dou,Shuo Li
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee
描述The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022..The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:.Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;.Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;.Part III: Breast imaging; colonoscopy; computer aided diagnosis;.Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;.Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;.Part VI: Image registration; image reconstruction;.Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; mach
出版日期Conference proceedings 2022
关键词animation; artificial intelligence; color image processing; computer vision; cross-computing tools and t
版次1
doihttps://doi.org/10.1007/978-3-031-16434-7
isbn_softcover978-3-031-16433-0
isbn_ebook978-3-031-16434-7Series 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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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022978-3-031-16434-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationerative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.
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