3 papers indexed
Zimu Zhang, Yiheng Zhong, Zhuoru Zhang, Yingzhen Hu, Yanan He, Fanliang Meng, et al.
Semi-supervised 3D medical image segmentation reduces the need for dense voxel-level annotations by exploiting unlabeled volumes. Although existing methods such as consistency regularization, pseudo-labeling, and co-training improve prediction-level robustness, they often provide…
Zhuoru Zhang, Yiheng Zhong, Zimu Zhang, Xiaofeng Liu
Recent advances in semi-supervised medical image segmentation have achieved remarkable performance through prediction consistency, pseudo-label supervision, and hard-region supervision. However, these methods primarily improve supervision quality rather than explicitly enforcing…
Yingzhen Hu, Yiheng Zhong, Keying Zhu, Zimu Zhang, Zihan Ye, Sifan Song, et al.
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 generation, while overlooking that prompt quality is fundamentally constrained by the expressiveness of…