Foundation models hold promise for generalizable analysis of structural brain magnetic resonance imaging (MRI) across development, aging and disease. However, existing models are typically built through centralized pretraining on pooled data, despite privacy and governance constraints. Such pooling optimization can ove...
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
Fine-grained recognition often involves hierarchical label spaces, where a model may be confident about a coarse semantic concept while remaining uncertain among its descendant classes. Such structured ambiguity requires uncertainty representations that capture both fine-grained classes and intermediate concepts. Howev...
Yuanye Liu, Xia-Hai Zhuang· 0 citations
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