Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops; ISIC 2023, Care-AI 2 M. Emre Celebi,Md Sirajus Salekin,

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书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops影响因子(影响力)




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops影响因子(影响力)学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops网络公开度




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops网络公开度学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops被引频次




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops被引频次学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops年度引用




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops年度引用学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops读者反馈




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops读者反馈学科排名

BULLY

Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillationnd identical distributions have not been fully addressed. The former problem arises from model parameter transfer between the server and clients, and the latter problem is due to differences in imaging protocols and operational customs. To reduce communication costs, dataset distillation methods hav

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AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation DatasetsLS, but they require more training data compared to convolutional neural networks (CNNs) due to their inherent parameter-heavy structure and lack of some inductive biases. To alleviate this issue, current approaches fine-tune pre-trained ViT backbones on SLS datasets, aiming to leverage the knowledg

Entreaty

Test-Time Selection for Robust Skin Lesion Analysisformation. Solutions that address this problem by regularizing models to prevent learning those spurious features achieve only partial success, and existing test-time debiasing techniques are inappropriate for skin lesion analysis due to either making unrealistic assumptions on the distribution of t

Perennial长期的

Global and Local Explanations for Skin Cancer Diagnosis Using Prototypesto provide explanations rely on saliency or attention maps that may not be easy to interpret. Moreover, the actual decision-making process is still a black-box. This paper proposes to overcome these limitations using class prototypes, both at the global (image-wide) and local (patch-based) levels. T

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阻挠

Generating Chinese Radiology Reports from X-Ray Images: A Public Dataset and an X-ray-to-Reports Genthese methods rely on annotations, posing a burden on healthcare professionals. In contrast, medical reports contain valuable information, leading to the emergence of Medical Reports Generation from Medical Images (MRGMI). Despite advancements, MRGMI predominantly focuses on English reports, lacking

VICT

慎重