Investigating the Fairness of Deep Learning Models in Breast Cancer Diagnosis Based on Race and Ethnicity
Year: 2024
Type: conference-paper
Abstract: Breast cancer is the leading cancer affecting women globally. Despite deep learning models making significant strides in diagnosing and treating this disease, ensuring fair outcomes across diverse populations presents a challenge, particularly when certain demographic groups are underrepresented in training datasets. Addressing the fairness of AI models across varied demographic backgrounds is crucial. This study analyzes demographic representation within the publicly accessible Emory Breast Imaging Dataset (EMBED), which includes de-identified mammography and clinical data. We spotlight the data disparities among racial and ethnic groups and assess the biases in mammography image classification models trained on this dataset, specifically ResNet-50 and Swin Transformer V2. Our evaluation of classification accuracies across these groups reveals significant variations in model performance, highlighting concerns regarding the fairness of AI diagnostic tools. This paper emphasizes the imperative need for fairness in AI and suggests directions for future research aimed at increasing the inclusiveness and dependability of these technologies in healthcare settings. Code is available at: https://github.com/kuanhuang0624/EMBEDFairModels.
Authors: Kuan Huang, Yingfeng Wang, Meng Xu
Institutions: Kean University, University of Tennessee at Chattanooga
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: National Science Foundation
Awards: Collaborative Research: CISE MSI: RCBP: SCH: Advancing Breast-Cancer Detection in Ultrasound Imaging through Active- and Weakly-Supervised Learning, Collaborative Research: CISE MSI: RCBP: SCH: Advancing Breast-Cancer Detection in Ultrasound Imaging through Active- and Weakly-Supervised Learning
OpenAlex ID: W4404187954