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

Hazardous

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops影響因子(影響力)




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops影響因子(影響力)學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops網(wǎng)絡(luò)公開度




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops被引頻次




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops被引頻次學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops年度引用




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops年度引用學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops讀者反饋




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops讀者反饋學(xué)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

Redundant

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長期的

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

objection

粘

阻撓

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