让AI学医生经验，提升肺结节检测的可靠性与可信度
Uncertainty-Aware Learning Policy for Reliable Pulmonary Nodule Detection on Chest X-Ray
- 结合医生临床经验与影像数据，学习不确定性感知的诊断策略
- 敏感性提升10%，在IoU 0.2/FPPI 2下达92%检测率，不确定性下降0.2
- 适合医疗AI可信度提升、临床辅助诊断系统研发人员参考
肺癌早期发现与及时干预至关重要。然而，胸片解读准确性受医生经验与疲劳程度影响显著。尽管医学AI迅速发展，但医生对其信任度仍低，制约了临床应用。这一疑虑核心源于对诊断不确定性的担忧。临床中医生依赖广泛背景知识与经验，而现有AI仅基于病变图像重复学习，缺乏综合判断能力，导致不确定性高。为此，本文提出不确定性感知学习策略，通过融合医生背景知识与胸片病变信息，弥补知识短板。实验使用2517张无病灶图像和656张结节图像（来自安山大学医院）。所提模型在IoU 0.2/FPPI 2指标下达到92%性能，较基线敏感性提升10%，同时熵值（不确定性度量）降低0.2。
原文摘要 · Abstract (English)
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.
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