提出高效可靠的肺部CT筛查框架，加速诊断同时保持高准确率。
Towards Clinical Practice in CT-Based Pulmonary Disease Screening: An Efficient and Reliable Framework
- 基于聚类的切片采样，兼顾代表性与多样性，减少计算量。
- 在2654例数据上实现超90%准确率与召回率，处理速度提升60%以上。
- 专设模糊性量化机制，识别细微病灶与伪影导致的不确定样本。
基于CT的肺部疾病自动筛查深度学习模型有望缓解放射科医生的巨大工作负担。然而，处理完整3D体积带来的高计算成本仍是临床广泛应用的主要障碍。现有子采样技术常因引入伪影或丢失关键信息而损害诊断完整性。为此，本文提出一种高效可靠框架（ERF），从根本上提升自动化CT分析的实用性。该框架包含两项核心创新：(1) 基于聚类的子采样（CSS）方法，通过优化代表性与多样性，高效选取紧凑且全面的切片子集；结合高效的k近邻搜索与迭代精炼流程，规避了以往方法的计算瓶颈，同时保留关键诊断特征。(2) 模糊性感知不确定性量化（AUQ）机制，针对由微小病灶和伪影引起的歧义问题增强可靠性；不同于常规不确定性度量，AUQ利用辅助分类器间的预测差异构建专门的模糊性评分，并在训练中最大化该差异，从而有效识别模型因视觉噪声或复杂病理而缺乏信心的样本。在两个公开数据集共2,654个CT体积上验证，ERF在多项肺部疾病诊断任务中达到与全体积分析相当的性能（准确率与召回率均超90%），同时将处理时间缩短60%以上。本工作标志着向快速、精准、可信的AI辅助筛查工具在时间敏感临床场景中部署迈出了重要一步。
原文摘要 · Abstract (English)
Deep learning models for pulmonary disease screening from Computed Tomography (CT) scans promise to alleviate the immense workload on radiologists. Still, their high computational cost, stemming from processing entire 3D volumes, remains a major barrier to widespread clinical adoption. Current sub-sampling techniques often compromise diagnostic integrity by introducing artifacts or discarding critical information. To overcome these limitations, we propose an Efficient and Reliable Framework (ERF) that fundamentally improves the practicality of automated CT analysis. Our framework introduces two core innovations: (1) A Cluster-based Sub-Sampling (CSS) method that efficiently selects a compact yet comprehensive subset of CT slices by optimizing for both representativeness and diversity. By integrating an efficient k-nearest neighbor search with an iterative refinement process, CSS bypasses the computational bottlenecks of previous methods while preserving vital diagnostic features. (2) An Ambiguity-aware Uncertainty Quantification (AUQ) mechanism, which enhances reliability by specifically targeting data ambiguity arising from subtle lesions and artifacts. Unlike standard uncertainty measures, AUQ leverages the predictive discrepancy between auxiliary classifiers to construct a specialized ambiguity score. By maximizing this discrepancy during training, the system effectively flags ambiguous samples where the model lacks confidence due to visual noise or intricate pathologies. Validated on two public datasets with 2,654 CT volumes across diagnostic tasks for 3 pulmonary diseases, ERF achieves diagnostic performance comparable to the full-volume analysis (over 90% accuracy and recall) while reducing processing time by more than 60%. This work represents a significant step towards deploying fast, accurate, and trustworthy AI-powered screening tools in time-sensitive clinical settings.
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