用深度集成提升肺部疾病诊断的可信度，让模型知道自己的不确定。
Enhancing Multi-Label Thoracic Disease Diagnosis with Deep Ensemble-Based Uncertainty Quantification
- 采用9个成员的深度集成，显著提升预测可靠性。
- 平均AUROC达0.8559，期望校准误差低至0.0728。
- 可分解出数据噪声与模型知识不足两类不确定性，适合临床使用。
深度学习模型（如CheXNet）在高风险临床场景中的应用受限于其确定性输出，无法提供可靠的预测置信度。本研究针对此问题，在NIH ChestX-ray14数据集上构建一个高性能的14种胸腔疾病诊断平台，并集成鲁棒的不确定性量化（UQ）机制。初始采用蒙特卡洛丢弃（MCD）方法时，期望校准误差（ECE）高达0.7588，性能不稳定。为此，转向高多样性9成员深度集成（DE），成功稳定性能并实现卓越可靠性：平均受试者工作特征曲线下面积（AUROC）达0.8559，平均F1分数为0.3857。关键成果包括均值ECE降至0.0728，负对数似然（NLL）为0.1916，并能可靠分解总不确定性为认知不确定性（均值0.0240）与随机不确定性，使模型从概率工具转变为可信的临床决策支持系统。
原文摘要 · Abstract (English)
The utility of deep learning models, such as CheXNet, in high stakes clinical settings is fundamentally constrained by their purely deterministic nature, failing to provide reliable measures of predictive confidence. This project addresses this critical gap by integrating robust Uncertainty Quantification (UQ) into a high performance diagnostic platform for 14 common thoracic diseases on the NIH ChestX-ray14 dataset. Initial architectural development failed to stabilize performance and calibration using Monte Carlo Dropout (MCD), yielding an unacceptable Expected Calibration Error (ECE) of 0.7588. This technical failure necessitated a rigorous architectural pivot to a high diversity, 9-member Deep Ensemble (DE). This resulting DE successfully stabilized performance and delivered superior reliability, achieving a State-of-the-Art (SOTA) average Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8559 and an average F1 Score of 0.3857. Crucially, the DE demonstrated superior calibration (Mean ECE of 0.0728 and Negative Log-Likelihood (NLL) of 0.1916) and enabled the reliable decomposition of total uncertainty into its Aleatoric (irreducible data noise) and Epistemic (reducible model knowledge) components, with a mean Epistemic Uncertainty (EU) of 0.0240. These results establish the Deep Ensemble as a trustworthy and explainable platform, transforming the model from a probabilistic tool into a reliable clinical decision support system.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。