Sharing medical image sets is essential yet challenging due to limited resource and strict regulatory, which significantly hinder the development of medical foundation models and the transfer of medical domain knowledge. In this paper, we utilize medical image dataset distillation techniques to enable effective model c...
Zhennaan Yu, Yin-Song Xu, Jia-Yi Xie et al.· IEEE Transactions on Pattern...· 0 citations
BrainFedFM, a structural brain MRI foundation model federatively pretrained on 164,707 three-dimensional scans drawn from diverse real-world data distributions and organized across 42 federated sites, demonstrates the generalizability of BrainFedFM and highlights federated pretraining as a practical strategy for develo...
Zhennaan Yu, Yang Liu, Xia-Hai Zhuang et al.· 0 citations
Image decomposition aims to analyze an image into elementary components, which is essential for numerous downstream tasks and also by nature provides certain interpretability to the analysis. Deep learning can be powerful for such tasks, but surprisingly their combination with a focus on interpretability and generaliza...
Si-Han Wang, Shang-Qi Gao, Fuping Wu et al.· IEEE Transactions on Image P...· 0 citations
The pursuit of decision safety in clinical applications calls for medical image classification models that are not only accurate but also transparent enough for clinicians to audit the visual evidence and decision logic behind each diagnosis. Existing concept-based models ground predictions in human-interpretable conce...
Yi-Bo Gao, Hang-Qi Zhou, Zhe-Yao Gao et al.· Medical Image Analysis· 0 citations
The resulting model, H$^2$EDL, functions as a hierarchical classifier that preserves consistency across different levels of the label tree that defines a valid tree-structured hyper-opinion, where the mass assigned to each node represents the belief that reaches that node but does not provide sufficient confidence to f...
Yuanye Liu, Xia-Hai Zhuang· 0 citations
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