4 citations · 9 across the 8 of their papers we have counts for
9 papers
PDEAgent-Bench: A Multi-Metric, Multi-Library Benchmark for PDE Solver Generation
Zhen Hang, Yushan Yashengjiang, Junhui Li +21
PDE-to-solver code generation aims to automatically synthesize executable numerical solvers from partial differential equation (PDE) specifications. This task requires not only und…
DUCX: Decomposing Unfairness in Tool-Using Chest X-ray Agents
Zikang Xu, Ruinan Jin, Xiaoxiao Li
Fairness in medical agents is becoming critical as tool-using clinical AI systems orchestrate specialized vision and language modules for tasks such as chest X-ray question answeri…
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…
MedAtlas: Evaluating LLMs for Multi-Round, Multi-Task Medical Reasoning Across Diverse Imaging Modalities and Clinical Text
Ronghao Xu, Zhen Huang, Yangbo Wei +5
Artificial intelligence has demonstrated significant potential in clinical decision-making; however, developing models capable of adapting to diverse real-world scenarios and perfo…
Style-Aligned Image Composition for Robust Detection of Abnormal Cells in Cytopathology
Qiuyi Qi, Xin Li, Ming Kong +4
Challenges such as the lack of high-quality annotations, long-tailed data distributions, and inconsistent staining styles pose significant obstacles to training neural networks to…
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
Ruinan Jin, Zikang Xu, Yuan Zhong +4
The advent of foundation models (FMs) in healthcare offers unprecedented opportunities to enhance medical diagnostics through automated classification and segmentation tasks. Howev…
APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation
Zikang Xu, Fenghe Tang, Quan Quan +2
Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort has been dedicated to mitigating unfairness in the training datasets or procedures. Ho…
Inspecting Model Fairness in Ultrasound Segmentation Tasks
Zikang Xu, Fenghe Tang, Quan Quan +3
With the rapid expansion of machine learning and deep learning (DL), researchers are increasingly employing learning-based algorithms to alleviate diagnostic challenges across dive…
Slide-SAM: Medical SAM Meets Sliding Window
Quan Quan, Fenghe Tang, Zikang Xu +2
The Segment Anything Model (SAM) has achieved a notable success in two-dimensional image segmentation in natural images. However, the substantial gap between medical and natural im…