14 citations · 75 across the 19 of their papers we have counts for
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cs.CV2026
Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Linghao Zhuang +11
Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Exis…
cs.CV2026
MedP-CLIP: Medical CLIP with Region-Aware Prompt Integration
Jiahui Peng, He Yao, Jingwen Li +9
Contrastive Language-Image Pre-training (CLIP) has demonstrated outstanding performance in global image understanding and zero-shot transfer through large-scale text-image alignmen…