5 citations · 17 across the 16 of their papers we have counts for
16 papers
Representation-guided in-context learning for medical image interpretation with multimodal large language models
Minda Zhao, Fangyu Hu, Yan Luo +8
Medical image interpretation is central to diagnosis and care, yet adapting general-purpose multimodal large language models (MLLMs) often requires resource-intensive domain-specif…
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…
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Minghan Li, Congcong Wen, Yu Tian +5
Fairness remains a critical concern in healthcare, where unequal access to services and treatment outcomes can adversely affect patient health. While Federated Learning (FL) presen…
Evaluation of In Vivo Subject-Specific Mechanical Modeling of the Optic Nerve Head for Robust Assessment of Ocular Mechanics
Soumaya Ouhsousou, Lucy Q. Shen, Chhavi Saini +3
To establish the tissue regions necessary to accurately represent the mechanics of the optic nerve head (ONH), imaging data of the ONH from 2 healthy subjects were used to create i…
TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction
Leila Gheisi, Henry Chu, Raju Gottumukkala +4
The use of artificial intelligence (AI) in automated disease classification significantly reduces healthcare costs and improves the accessibility of services. However, this transfo…
On Demographic Group Fairness Guarantees in Deep Learning
Yan Luo, Congcong Wen, Min Shi +3
We present a theoretical framework analyzing the relationship between data distributions and fairness guarantees in equitable deep learning. We establish novel bounds that account…
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…
FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
Yu Tian, Congcong Wen, Min Shi +6
Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have intr…
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…
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…