通过双分类器识别高危错误，提升医疗模型安全性。
Improving Model Safety by Targeted Error Correction
- 用双分类器GBDT区分普通误判与危险非人类误判。
- 在三大数据集上减少34.1%高危错误，诊断安全达92.13%。
- 无需重训模型，推理延迟仅增加1.84%，适合真实部署。
机器学习在关键应用中的普及要求有效缓解高后果错误。本文提出一种基于双分类器GBDT的流水线方法，用于区分常规人类类错误与高风险非人类误判。在动物品种分类、皮肤病变诊断（ISIC 2018）和前列腺组织病理学（SICAPv2）三个领域评估中，该框架展现出稳健的安全性提升。为应对实际部署挑战，结果表明该流水线引入的推理延迟极低（动物数据集为1.60%，ISIC为1.84%，SICAPv2为1.70%），同时在修正精度上优于传统的最大类别概率（MCP）基线。保守修正策略成功将ISIC中的危险非人类错误降低34.1%，SICAPv2降低12.57%，使超类诊断安全率分别提升至90.41%和92.13%。这证明了在不进行昂贵模型重训的前提下，可显著提升安全关键系统的可靠性。
原文摘要 · Abstract (English)
The widespread adoption of machine learning in critical applications demands techniques to mitigate high-consequence errors. Our method utilizes a dual-classifier GBDT pipeline to distinguish routine human-like errors from high-risk non-human misclassifications. Evaluated across three domains, animal breed classification, skin lesion diagnosis (ISIC 2018), and prostate histopathology (SICAPv2), our framework demonstrates robust safety improvements. To address real-world deployment concerns, our results confirm the pipeline introduces negligible inference latency (1.60% overhead for the animal dataset, 1.84% for ISIC, and 1.70% for SICAPv2) while outperforming traditional Maximum Class Probability (MCP) baselines in correction precision. Our conservative correction strategy successfully reduced dangerous non-human errors by 34.1% in ISIC and 12.57% in SICAPv2, improving super-class diagnostic safety to 90.41% and 92.13% respectively. This proves that safety-critical reliability can be substantially enhanced post-hoc without expensive model retraining. keywords: Error Analysis, Post-hoc Correction, Trustworthy AI.
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。