5 citations · 14 across the 8 of their papers we have counts for
8 papers
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…
Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning
Yan Luo, Min Shi, Yu Tian +2
Glaucoma is the number one cause of irreversible blindness globally. A major challenge for accurate glaucoma detection and progression forecasting is the bottleneck of limited labe…
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
Yan Luo, Yu Tian, Min Shi +5
Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated p…
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images
Min Shi, Anagha Lokhande, Mojtaba S. Fazli +10
Ophthalmic images and derivatives such as the retinal nerve fiber layer (RNFL) thickness map are crucial for detecting and monitoring ophthalmic diseases (e.g., glaucoma). For comp…
Perception-Aligned AI Outputs: End-to-End Visual Prediction for Uncertainty Communication in Clinical Decision-Making
Mohammad Eslami, Solale Tabarestani, Saber Kazeminasab +9
Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians…
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images
Farzin Soleymani, Mohammad Eslami, Tobias Elze +2
We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images. DVC simultaneously learns the multiv…