Patient Aware Active Learning for Fine-Grained OCT Classification

TL;DR

This paper introduces a clinically interpretable active learning framework for OCT classification that incorporates patient insights, improving performance and usability in medical settings compared to traditional uncertainty-based methods.

Contribution

The paper presents a novel active learning framework that integrates clinical insights, making it more suitable for medical use and enhancing classification performance.

Findings

Outperforms five common active learning paradigms on two architectures.

Effectively captures diverse disease manifestations from patients.

Compatible with existing medical practices and workflows.

Abstract

This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing frameworks have primarily used mathematical constructs to engineer uncertainty or diversity-based methods for selecting the most informative samples. However, such algorithms do not present themselves naturally as usable by the medical community and healthcare providers. Thus, their deployment in clinical settings is very limited, if any. For this purpose, we propose a framework that incorporates clinical insights into the sample selection process of active learning that can be incorporated with existing algorithms. Our medically interpretable active learning framework captures diverse disease manifestations from patients to improve generalization performance of OCT classification. After comprehensive experiments, we report that incorporating patient insights within the active learning framework yields performance that matches or surpasses five commonly used paradigms on two architectures with a dataset having imbalanced patient distributions. Also, the framework integrates within existing medical practices and thus can be used by healthcare providers.