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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

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发表于 2025-3-21 17:48:41 | 显示全部楼层 |阅读模式
书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
副标题26th International C
编辑Hayit Greenspan,Anant Madabhushi,Russell Taylor
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 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay
描述.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 applicatio
出版日期Conference proceedings 2023
关键词Computer Science; Informatics; Conference Proceedings; Research; Applications
版次1
doihttps://doi.org/10.1007/978-3-031-43990-2
isbn_softcover978-3-031-43989-6
isbn_ebook978-3-031-43990-2Series 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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978-3-031-43989-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms Using Self-adversariat our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.
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A Spatial-Temporally Adaptive PINN Framework for 3D Bi-Ventricular Electrophysiological Simulations enable solutions over longer time domains. We experimentally demonstrated the effectiveness of the presented PINN framework to obtain the complete forward and inverse EP solutions over the 3D bi-ventricular geometry, which is otherwise not possible with vanilla PINN frameworks.
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Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignmenbi-directional transport to align the target features with class prototypes by minimizing its expected cost. On top of that, a contrastive learning stage is further devised to utilize those pixels with unreliable predictions for a more compact target feature distribution. Extensive experiments on a
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