Uncertainty-Aware Learning Policy for Reliable Pulmonary Nodule Detection on Chest X-Ray

TL;DR

This paper introduces an uncertainty-aware learning policy for pulmonary nodule detection on chest X-rays, improving diagnostic accuracy and reducing uncertainty by incorporating physician-like background knowledge into AI models.

Contribution

The study proposes a novel learning policy that integrates background knowledge with lesion information, enhancing AI reliability in pulmonary nodule detection.

Findings

Achieved 92% detection performance with IoU 0.2 and FPPI 2.

Improved sensitivity by 10% over baseline.

Reduced model uncertainty by decreasing entropy by 0.2.

Abstract

Early detection and rapid intervention of lung cancer are crucial. Nonetheless, ensuring an accurate diagnosis is challenging, as physicians' ability to interpret chest X-rays varies significantly depending on their experience and degree of fatigue. Although medical AI has been rapidly advancing to assist in diagnosis, physicians' trust in such systems remains limited, preventing widespread clinical adoption. This skepticism fundamentally stems from concerns about its diagnostic uncertainty. In clinical diagnosis, physicians utilize extensive background knowledge and clinical experience. In contrast, medical AI primarily relies on repetitive learning of the target lesion to generate diagnoses based solely on that data. In other words, medical AI does not possess sufficient knowledge to render a diagnosis, leading to diagnostic uncertainty. Thus, this study suggests an Uncertainty-Aware Learning Policy that can address the issue of knowledge deficiency by learning the physicians' background knowledge alongside the Chest X-ray lesion information. We used 2,517 lesion-free images and 656 nodule images, all obtained from Ajou University Hospital. The proposed model attained 92% (IoU 0.2 / FPPI 2) with a 10% enhancement in sensitivity compared to the baseline model while also decreasing entropy as a measure of uncertainty by 0.2.