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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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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applied computing; life and medical sciences; computational biology; computer vision; computing methodol
版次1
doihttps://doi.org/10.1007/978-3-031-43987-2
isbn_softcover978-3-031-43986-5
isbn_ebook978-3-031-43987-2Series 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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Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to automatically discover and identify new semantic categories from new data. In this paper, we propose a new novel class discovery
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A Style Transfer-Based Augmentation Framework for Improving Segmentation and Classification Performassification performance of deep learning models for ultrasound image analysis. Previous studies have attempted to solve this problem by using style transfer and augmentation techniques, but these methods usually require a large amount of data from multiple sources and source-specific discriminators,
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SegNetr: Rethinking the Local-Global Interactions and Skip Connections in U-Shaped Networks-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network; 2) simply fuse the features of e
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Multi-modality Contrastive Learning for Sarcopenia Screening from Hip X-rays and Clinical Informatiormal muscle strength. Accurate screening for sarcopenia is a key process of clinical diagnosis and therapy. In this work, we propose a novel multi-modality contrastive learning (MM-CL) based method that combines hip X-ray images and clinical parameters for sarcopenia screening. Our method captures t
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DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classificationer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medica
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