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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…
Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models
Jialin Wu, Qianru Zhang, Georges El Fakhri +1
Longitudinal medical visual question answering (VQA) requires reasoning about anatomical differences between an image of a current time point and an image of a referred time point.…
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation
Xiaofeng Liu, Qianru Zhang, Thibault Marin +4
The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic i…
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning
Yuan Zhao, Youwei Pang, Jiaming Zuo +10
Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the notion of a concept remains…
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models
Nhan Ho, Luu Le, Thanh-Huy Nguyen +3
Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge for foundation segmentation m…
Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift
Xiaofeng Liu, Menghua Xia, Yanis Chemli +3
In this work, we propose an unsupervised domain adaptation (UDA) framework for 3D volumetric lesion detection that adapts a detector trained on labeled FDG PET/CT to unlabeled PSMA…
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…