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Found 4 papers, 1 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.
MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation
Furthermore, via experiments on the NYUv2 and IBims-1 datasets, we demonstrate that these enhanced representations translate to performance improvements in both the in-distribution and out-of-distribution settings.
Ranked #10 on
Monocular Depth Estimation
on NYU-Depth V2
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.
Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis
In this paper, we present the first comprehensive study that discovers effective fine-tuning strategies for self-supervised learning in medical imaging.
