8 papers · 1 filter
VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation
Zimu Zhang, Yiheng Zhong, Zhuoru Zhang +4
Semi-supervised 3D medical image segmentation reduces the need for dense voxel-level annotations by exploiting unlabeled volumes. Although existing methods such as consistency regu…
SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation
Zhuoru Zhang, Yiheng Zhong, Zimu Zhang +1
Recent advances in semi-supervised medical image segmentation have achieved remarkable performance through prediction consistency, pseudo-label supervision, and hard-region supervi…
HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation
Yingzhen Hu, Yiheng Zhong, Keying Zhu +5
Prompt-free adaptation of the Segment Anything Model (SAM) has emerged as a promising paradigm for automatic medical image segmentation. Existing methods mainly focus on prompt gen…
SAIF: A Stability-Aware Inference Framework for Medical Image Segmentation with Segment Anything Model
Ke Wu, Shiqi Chen, Yiheng Zhong +5
Segment Anything Model (SAM) enable scalable medical image segmentation but suffer from inference-time instability when deployed as a frozen backbone. In practice, bounding-box pro…
Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation
Yingxue Su, Yiheng Zhong, Keying Zhu +7
Medical image segmentation is critical for computer-aided diagnosis. However, dense pixel-level annotation is time-consuming and costly, and medical datasets often exhibit severe c…
SAM-DCE: Decoupled Context Enhancement for Prompt-Free Medical Image Segmentation
Yingzhen Hu, Yiheng Zhong, Ruobing Li +8
The Segment Anything Model (SAM) is a promising foundation model for medical image segmentation, but its prompt-driven design is not directly suitable for fully automatic multi-cla…
PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation
Yiheng Zhong, Zihong Luo, Chengzhi Liu +7
Segment Anything Model (SAM) demonstrates powerful zero-shot capabilities; however, its accuracy and robustness significantly decrease when applied to medical image segmentation. E…
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Feilong Tang, Zhongxing Xu, Ming Hu +6
In medical image analysis, multi-organ semi-supervised segmentation faces challenges such as insufficient labels and low contrast in soft tissues. To address these issues, existing…