ys-zong/MEDFAIR
[ICLR 2023 spotlight] MEDFAIR: Benchmarking Fairness for Medical Imaging
This project helps medical researchers and practitioners evaluate if their AI models for medical imaging are fair across different demographic groups. It takes common medical image datasets and associated patient demographic data as input. The output helps users understand and compare how different fairness algorithms perform in reducing bias, ensuring AI models don't inadvertently disadvantage certain patient populations based on factors like age, sex, or race.
No commits in the last 6 months.
Use this if you are a medical researcher or data scientist building or evaluating AI models for medical image analysis and want to rigorously test and improve their fairness across diverse patient populations.
Not ideal if you are looking for a pre-trained, production-ready diagnostic tool or a general-purpose image analysis library unrelated to fairness benchmarking.
Stars
73
Forks
15
Language
Python
License
—
Category
Last pushed
May 22, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ys-zong/MEDFAIR"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations...
holistic-ai/holisticai
This is an open-source tool to assess and improve the trustworthiness of AI systems.
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment...
datamllab/awesome-fairness-in-ai
A curated list of awesome Fairness in AI resources