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Found 8 papers, 6 papers with code
FairCLIP: Harnessing Fairness in Vision-Language Learning
Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions.
FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling
Existing medical fairness datasets are all for classification tasks, and no fairness datasets are available for medical segmentation, while medical segmentation is an equally important clinical task as classifications, which can provide detailed spatial information on organ abnormalities ready to be assessed by clinicians.
FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes.
Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning
This is the largest glaucoma detection dataset with 3D OCT imaging data and the first glaucoma progression forecasting dataset that is publicly available.
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection.
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images
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).
Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning
With the advent of sophisticated machine learning (ML) techniques and the promising results they yield, especially in medical applications, where they have been investigated for different tasks to enhance the decision-making process.
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images
We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images.
