1 citations · 1 across the 2 of their papers we have counts for
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Glance and Focus Reinforcement for Pan-cancer Screening
Linshan Wu, Jiaxin Zhuang, Hao Chen
Pan-cancer screening in large-scale CT scans remains challenging for existing AI methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT vo…
UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
Linshan Wu, Yuxiang Nie, Sunan He +12
The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. Howev…
MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training
Xuefeng Ni, Linshan Wu, Jiaxin Zhuang +6
3D medical image analysis is pivotal in numerous clinical applications. However, the scarcity of labeled data and limited generalization capabilities hinder the advancement of AI-e…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
Large-Scale 3D Medical Image Pre-training with Geometric Context Priors
Linshan Wu, Jiaxin Zhuang, Hao Chen
The scarcity of annotations poses a significant challenge in medical image analysis. Large-scale pre-training has emerged as a promising label-efficient solution, owing to the util…