5 citations · 17 across the 16 of their papers we have counts for
9 papers · 1 filter
Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
Liyin Chen, Nazlee Zebardast, Souvick Mukherjee +4
Stochastic generative models are increasingly used for longitudinal imaging, but their added complexity may provide limited benefit when predictable disease-related change is small…
FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation
Yan Luo, Muhammad Osama Khan, Congcong Wen +6
Recent progress in generative AI, especially diffusion models, has demonstrated significant utility in text-to-image synthesis. Particularly in healthcare, these models offer immen…
FairDiff: Fair Segmentation with Point-Image Diffusion
Wenyi Li, Haoran Xu, Guiyu Zhang +4
Fairness is an important topic for medical image analysis, driven by the challenge of unbalanced training data among diverse target groups and the societal demand for equitable med…
FairCLIP: Harnessing Fairness in Vision-Language Learning
Yan Luo, Min Shi, Muhammad Osama Khan +9
Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions. Although fairness has been investigated i…
FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling
Yu Tian, Min Shi, Yan Luo +3
Fairness in artificial intelligence models has gained significantly more attention in recent years, especially in the area of medicine, as fairness in medical models is critical to…
FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
Yan Luo, Muhammad Osama Khan, Yu Tian +5
Equity in AI for healthcare is crucial due to its direct impact on human well-being. Despite advancements in 2D medical imaging fairness, the fairness of 3D models remains underexp…