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20242026
most citedSelf-Paced Sample Selection for Barely-Supervised Medical Image Segmentation

1 citations · 1 across the 6 of their papers we have counts for

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8 papers

cs.CV2026

Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models

Tran Dinh Tien, Zhiqiang Shen

Current prompt-based and adapter-based tuning of vision-language models (VLMs) is attractive for medical imaging, where clinical data sensitivity favors frozen backbones and annota…

eess.IV2026

Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation

Jianqiang Lin, Zhiqiang Shen, Peng Cao +3

Although diffusion models have achieved remarkable progress in multi-modal magnetic resonance imaging (MRI) translation tasks, existing methods still tend to suffer from anatomical…

cs.CV2025

SynMatch: Rethinking Consistency in Medical Image Segmentation with Sparse Annotations

Zhiqiang Shen, Peng Cao, Xiaoli Liu +2

Label scarcity remains a major challenge in deep learning-based medical image segmentation. Recent studies use strong-weak pseudo supervision to leverage unlabeled data. However, p…

eess.IV2025

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation

Xi Chen, Zhiqiang Shen, Peng Cao +2

Medical images are usually collected from multiple domains, leading to domain shifts that impair the performance of medical image segmentation models. Domain Generalization (DG) ai…

eess.IV2025

Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation

Zhiqiang Shen, Peng Cao, Jinzhu Yang +2

Due to domain shifts across diverse medical imaging modalities, learned segmentation models often suffer significant performance degradation during deployment. We posit that these…

cs.CV20241 cited

Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation

Junming Su, Zhiqiang Shen, Peng Cao +2

The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate…