Mitigating Feature Bias in DL Models for Cervical Cytology

• Authors: Shubhashree Sahu, Shubham Ojha & Aditya Narendra

• Venue & Year: Conference on Neural Information Processing Systems (NeurIPS), 2024

Abstract

Cervical cancer poses a serious health risk to women all across the world. The advancements in deep learning (DL), have driven the rise of DL-assisted cervical cytology screening methods for various diagnostic tasks. However, most of these DL approaches are susceptible to the inherent bias in clinical datasets, which restricts their practical deployment. A key source of bias in DL-based cervical cytology workflows is the high variability in the representation of various features extracted by these models across different classes. This imbalanced feature representation results in inconsistent model performance across different feature cohorts, which is known as feature bias. Building on this understanding, our work underscores the importance of mitigating feature bias in DL-based cervical cytology workflows. We demonstrate that effective bias mitigation reduces skewness in performance metrics, which improves diagnostic performance and enhances patient outcomes.