45 citations · 46 across the 5 of their papers we have counts for
6 papers
SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation
Linkuan Zhou, Yinghao Xia, Yufei Shen +6
Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments. Existing methods are fundamentally limited, suffer…
Cross-Stain Contrastive Learning for Paired Immunohistochemistry and Histopathology Slide Representation Learning
Yizhi Zhang, Lei Fan, Zhulin Tao +4
Universal, transferable whole-slide image (WSI) representations are central to computational pathology. Incorporating multiple markers (e.g., immunohistochemistry, IHC) alongside H…
CMI-MTL: Cross-Mamba interaction based multi-task learning for medical visual question answering
Qiangguo Jin, Xianyao Zheng, Hui Cui +7
Medical visual question answering (Med-VQA) is a crucial multimodal task in clinical decision support and telemedicine. Recent self-attention based methods struggle to effectively…
ERSR: An Ellipse-constrained pseudo-label refinement and symmetric regularization framework for semi-supervised fetal head segmentation in ultrasound images
Linkuan Zhou, Zhexin Chen, Yufei Shen +9
Automated segmentation of the fetal head in ultrasound images is critical for prenatal monitoring. However, achieving robust segmentation remains challenging due to the poor qualit…
Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation
Qiangguo Jin, Hui Cui, Junbo Wang +7
Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-l…
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Haolong Jin, Shenglin Liu, Cong Cong +4
Federated learning (FL) has emerged as a promising approach for collaborative medical image analysis, enabling multiple institutions to build robust predictive models while preserv…