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.
1 code implementation • 3 Nov 2023 • Yu Tian, Min Shi, Yan Luo, Ava Kouhana, Tobias Elze, Mengyu Wang
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.
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 • ICCV 2023 • Yan Luo, Min Shi, Yu Tian, Tobias Elze, Mengyu Wang
This is the largest glaucoma detection dataset with 3D OCT imaging data and the first glaucoma progression forecasting dataset that is publicly available.
1 code implementation • 15 Jun 2023 • Yan Luo, Yu Tian, Min Shi, Louis R. Pasquale, Lucy Q. Shen, Nazlee Zebardast, Tobias Elze, Mengyu Wang
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.
1 code implementation • 2 Sep 2022 • Min Shi, Anagha Lokhande, Mojtaba S. Fazli, Vishal Sharma, Yu Tian, Yan Luo, Louis R. Pasquale, Tobias Elze, Michael V. Boland, Nazlee Zebardast, David S. Friedman, Lucy Q. Shen, Mengyu Wang
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).
1 code implementation • 9 May 2022 • Mohammad Eslami, Solale Tabarestani, Ehsan Adeli, Glyn Elwyn, Tobias Elze, Mengyu Wang, Nazlee Zebardast, Nassir Navab, Malek Adjouadi
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.
1 code implementation • 22 Sep 2021 • Farzin Soleymani, Mohammad Eslami, Tobias Elze, Bernd Bischl, Mina Rezaei
We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images.