Patient Aware Active Learning for Fine-Grained OCT Classification
This work addresses the limited deployment of active learning in clinical practice by providing a medically interpretable framework for healthcare providers, though it is incremental as it builds on existing algorithms.
The paper tackled the problem of making active learning more usable in medical settings by incorporating clinical insights into sample selection, resulting in performance that matches or surpasses five common paradigms on two architectures with imbalanced patient data.
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.