1 citations · 2 across the 13 of their papers we have counts for
11 papers · 1 filter
ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training
Rongsheng Wang, Fenghe Tang, Zihang Jiang +10
Learning transferable and interpretable representations from medical volumetric scans remains challenging due to complex anatomical structures and weak, heterogeneous supervision p…
Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation
Haoyun Chen, Fenghe Tang, Wenxin Ma +1
Universal medical image segmentation seeks to use a single foundational model to handle diverse tasks across multiple imaging modalities. However, existing approaches often rely he…
CausalSpatial: A Benchmark for Object-Centric Causal Spatial Reasoning
Wenxin Ma, Chenlong Wang, Ruisheng Yuan +6
Humans can look at a static scene and instantly predict what happens next -- will moving this object cause a collision? We call this ability Causal Spatial Reasoning. However, curr…
Equivariant Sampling for Improving Diffusion Model-based Image Restoration
Chenxu Wu, Qingpeng Kong, Peiang Zhao +5
Recent advances in generative models, especially diffusion models, have significantly improved image restoration (IR) performance. However, existing problem-agnostic diffusion mode…
U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking
Fenghe Tang, Chengqi Dong, Wenxin Ma +7
Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread a…
More performant and scalable: Rethinking contrastive vision-language pre-training of radiology in the LLM era
Yingtai Li, Haoran Lai, Xiaoqian Zhou +4
The emergence of Large Language Models (LLMs) presents unprecedented opportunities to revolutionize medical contrastive vision-language pre-training. In this paper, we show how LLM…