患者级别肘部异常检测：学习预处理、校准和分诊导向操作点的泄漏感知评估
Patient-Level Elbow Abnormality Detection: Leakage-Aware Evaluation of Learned Preprocessing, Calibration, and Triage-Oriented Operating Points
AI总结：
本研究在MURA数据集的肘部X光片上，采用泄漏感知协议评估学习预处理对患者级别肌肉骨骼异常检测的影响，发现预处理增益有限且依赖配置，原始输入DenseNet121基线仍具竞争力。
摘要：
在本研究中，我们使用MURA数据集的肘部X光片，在分诊导向的骨科异常检测任务中检查学习预处理流程。评估重点是在泄漏感知协议下进行患者级别的肌肉骨骼异常检测。我们比较了多个预处理流程，包括有和没有轻量级DnCNN模块作为学习预处理组件的情况，以评估它们对区分和校准的影响。性能通过区分度指标（AUROC、PR-AUC）、校准指标（ECE、Brier分数）以及针对高特异性的验证选择操作点分析进行评估。结果表明，不同预处理策略之间的差异很小且依赖于配置，与原始输入DenseNet121基线相比，没有一致的区分度优势。原始和多样化输入与DnCNN前端结合显示出降低的ECE和Brier分数，而CLAHE与DnCNN结合并未改善校准。总体而言，结果表明在患者级别评估下，预处理增益有限且依赖于配置；原始输入DenseNet121基线在整个过程中保持竞争力，并且没有测试的预处理策略在所有指标上产生一致的区分度优势。
英文摘要：
In this study, we examine learned preprocessing pipelines in the context of triage-oriented orthopedic abnormality detection task using elbow radiographs from MURA dataset. The evaluation focuses on patient-level detection of musculoskeletal abnormalities under a leakage-aware protocol. We compare multiple preprocessing pipelines, with and without a lightweight DnCNN module as a learned preprocessing component, to assess their impact on discrimination and calibration. Performance is assessed using discrimination metrics (AUROC, PR-AUC), calibration measures (ECE, Brier score), and validation-selected operating point analysis targeting high specificity. Results show that differences across preprocessing strategies are modest and configuration-dependent, with no consistent discrimination advantage over the raw-input DenseNet121 baseline. The raw and diverse inputs combined with the DnCNN front-end showed reduced ECE and Brier score, while CLAHE combined with DnCNN did not improve calibration. Overall, the results suggest that under patient-level evaluation, preprocessing gains are modest and configuration-dependent; the raw-input DenseNet121 baseline remains competitive throughout, and no tested preprocessing strategy produced a consistent discrimination advantage across all metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
