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20242026
most citedOn hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)

1 citations · 1 across the 14 of their papers we have counts for

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cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

Rethinking Evaluation of Infrared Small Target Detection

Youwei Pang, Xiaoqi Zhao, Lihe Zhang +4

As an essential vision task, infrared small target detection (IRSTD) has seen significant advancements through deep learning. However, critical limitations in current evaluation pr…

cs.CV2025

UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation

Xiaoqi Zhao, Youwei Pang, Chenyang Yu +5

Multi-modal image segmentation faces real-world deployment challenges from incomplete/corrupted modalities degrading performance. While existing methods address training-inference…