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A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation
Jianghao Wu, Xiangde Luo, Yubo Zhou +3
Test-Time Adaptation (TTA) offers a practical solution for deploying image segmentation models under domain shift without accessing source data or retraining. Among existing TTA st…
MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation
Yiwen Ye, Yicheng Wu, Xiangde Luo +5
Foundation models have become a promising paradigm for advancing medical image analysis, particularly for segmentation tasks where downstream applications often emerge sequentially…
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
Han Zhang, Xiangde Luo, Yong Chen +1
Annotation variability remains a substantial challenge in medical image segmentation, stemming from ambiguous imaging boundaries and diverse clinical expertise. Traditional deep le…
Dynamic Gradient Sparsification Training for Few-Shot Fine-tuning of CT Lymph Node Segmentation Foundation Model
Zihao Luo, Zijun Gao, Wenjun Liao +3
Accurate lymph node (LN) segmentation is critical in radiotherapy treatment and prognosis analysis, but is limited by the need for large annotated datasets. While deep learning-bas…
Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning
Litingyu Wang, Yijie Qu, Xiangde Luo +3
Assessing the presence of potentially malignant lymph nodes aids in estimating cancer progression, and identifying surrounding benign lymph nodes can assist in determining potentia…
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation
Zihao Luo, Xiangde Luo, Zijun Gao +1
Deep learning models have exhibited remarkable efficacy in accurately delineating the prostate for diagnosis and treatment of prostate diseases, but challenges persist in achieving…