Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods

• Main Authors: Marcinkevičs, Ričards, Ozkan, Ece, Vogt, Julia E
• Format: Journal Article
• Language: English
• Published: 26-07-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2208.00781

Deep neural networks for image-based screening and computer-aided diagnosis
have achieved expert-level performance on various medical imaging modalities,
including chest radiographs. Recently, several works have indicated that these
state-of-the-art classifiers can be biased with respect to sensitive patient
attributes, such as race or gender, leading to growing concerns about
demographic disparities and discrimination resulting from algorithmic and
model-based decision-making in healthcare. Fair machine learning has focused on
mitigating such biases against disadvantaged or marginalised groups, mainly
concentrating on tabular data or natural images. This work presents two novel
intra-processing techniques based on fine-tuning and pruning an already-trained
neural network. These methods are simple yet effective and can be readily
applied post hoc in a setting where the protected attribute is unknown during
the model development and test time. In addition, we compare several intra- and
post-processing approaches applied to debiasing deep chest X-ray classifiers.
To the best of our knowledge, this is one of the first efforts studying
debiasing methods on chest radiographs. Our results suggest that the considered
approaches successfully mitigate biases in fully connected and convolutional
neural networks offering stable performance under various settings. The
discussed methods can help achieve group fairness of deep medical image
classifiers when deploying them in domains with different fairness
considerations and constraints.