1 code implementation • 29 Mar 2024 • Yan Luo, Min Shi, Muhammad Osama Khan, Muhammad Muneeb Afzal, Hao Huang, Shuaihang Yuan, Yu Tian, Luo Song, Ava Kouhana, Tobias Elze, Yi Fang, Mengyu Wang
Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions.
no code implementations • 6 Oct 2023 • Muhammad Osama Khan, Junbang Liang, Chun-Kai Wang, Shan Yang, Yu Lou
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
no code implementations • 3 Oct 2023 • Yan Luo, Muhammad Osama Khan, Yu Tian, Min Shi, Zehao Dou, Tobias Elze, Yi Fang, Mengyu Wang
To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes.
no code implementations • 20 Jul 2023 • Muhammad Osama Khan, Yi Fang
In this paper, we present the first comprehensive study that discovers effective fine-tuning strategies for self-supervised learning in medical imaging.