4 citations · 6 across the 21 of their papers we have counts for
11 papers · 1 filter
Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges
Ariel Lubonja, Pedro R. A. S. Bassi, Wenxuan Li +4
Open challenges have become the de facto standard for comparative ranking of medical AI methods. Despite their importance, medical AI leaderboards exhibit three persistent limitati…
See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement
Junqi Liu, Zejun Wu, Pedro R. A. S. Bassi +15
Image enhancement improves visual quality and helps reveal details that are hard to see in the original image. In medical imaging, it can support clinical decision-making, but curr…
Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data
Qi Chen, Xinze Zhou, Chen Liu +11
AI for tumor segmentation is limited by the lack of large, voxel-wise annotated datasets, which are hard to create and require medical experts. In our proprietary JHH dataset of 3,…
Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks
Pedro R. A. S. Bassi, Xinze Zhou, Wenxuan Li +20
Early tumor detection save lives. Each year, more than 300 million computed tomography (CT) scans are performed worldwide, offering a vast opportunity for effective cancer screenin…
MTraining: Distributed Dynamic Sparse Attention for Efficient Ultra-Long Context Training
Wenxuan Li, Chengruidong Zhang, Huiqiang Jiang +3
The adoption of long context windows has become a standard feature in Large Language Models (LLMs), as extended contexts significantly enhance their capacity for complex reasoning…
Learning Segmentation from Radiology Reports
Pedro R. A. S. Bassi, Wenxuan Li, Jieneng Chen +8
Tumor segmentation in CT scans is key for diagnosis, surgery, and prognosis, yet segmentation masks are scarce because their creation requires time and expertise. Public abdominal…