Exploring the interplay of label bias with subgroup size and separability

This repository contains code associated with the paper:

E. A. M. Stanley, R. Mehta, M. Roschewitz, N. D. Forkert, B. Glocker. Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification. MICCAI Fairness of AI in Medical Imaging Workshop, 2025.

[arXiv][Proceedings]

Overview

• /data_preproc contains notebooks for filtering the metadata associated with the images, and applying label bias (as described in sections 2.1 and 2.2 of the paper).

• /models contains python files for training ResNet-18 models for breast density classification (mammo-net-density.py and mammo-net-density-label-bias.py), as well as imaging manufacturer (mammo-net-manufacturer.py) and pseudo-subgroup (mammo-net-pseudo-subgroup.py) classification. run_models.sh contains example commands for running these files.

• /analysis contains notebooks for evaluating subgroup separability (model_evaluation_separability.ipynb), evaluating breast density classification performance (model_evaluation_clean.ipynb and model_evaluation_labelbias.ipynb), and performing feature inspection along the first principal component from the penultimate layer of the trained models (model_inspection.ipynb).

The environment.yml file allows for installation of all dependencies in a conda environment.

Data

Data used in this work comes from the EMory BrEast imaging Dataset (EMBED). Data requests and documentation can be accessed here: github.com/Emory-HITI/EMBED_Open_Data/.