评估了多种预处理对肘部影像异常检测的提升效果，发现原始输入模型依然表现优异。
Patient-Level Elbow Abnormality Detection: Leakage-Aware Evaluation of Learned Preprocessing, Calibration, and Triage-Oriented Operating Points
- 对比带与不带轻量DnCNN的预处理流程，研究其在患者级检测中的表现差异
- 各策略间性能差异小，无统一优势；原始输入模型在所有指标上均具竞争力
- 强调泄露感知评估，适合关注临床分诊准确性的医学图像研究者参考
本研究基于MURA数据集中的肘部放射影像，考察学习型预处理流程在面向分诊的骨科异常检测任务中的表现。评估聚焦于患者级别的肌肉骨骼异常检测，并采用泄露感知协议。比较了多种预处理流程（含与不含轻量DnCNN模块）对判别能力与校准性的影响。性能通过判别指标（AUROC、PR-AUC）、校准度量（ECE、Brier score）及高特异性目标的验证选择操作点分析进行评估。结果显示，不同预处理策略间的差异微小且依赖配置，未见显著优于原始输入DenseNet121基线的判别优势。原始输入与多样输入结合DnCNN前端时，ECE和Brier得分降低；而CLAHE结合DnCNN未改善校准性。总体表明，在患者级评估下，预处理收益有限且依赖配置，原始输入基线始终具备竞争力，任一测试策略均未在所有指标上展现一致优势。
原文摘要 · Abstract (English)
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.
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