分析乳腺肿块的可解释模型的多位点验证。

IF 2.6 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0320091
Luke Moffett, Alina Jade Barnett, Jon Donnelly, Fides Regina Schwartz, Hari Trivedi, Joseph Lo, Cynthia Rudin
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引用次数: 0

摘要

iaia - bl是一种基于深度学习的、内在可解释的乳腺病变恶性预测模型,对两组患者进行了外部验证:207名31至96岁的女性(425张乳房x光片)来自iCAD, 58名女性(104张乳房x光片)来自埃默里大学。这是对固有可解释的、基于深度学习的病变分类模型的首次外部验证。以AUC衡量,IAIA-BL和黑箱基线模型在外部数据集上的质量裕度分类性能低于内部数据集。这些损失与恶性肿瘤分类性能的较小降低相关,尽管所有部位的AUC 95%置信区间重叠。然而,通过对病变相关部分的模型激活来测量的可解释性在所有人群中都保持不变。总之,这些结果表明,即使在性能不具备的情况下,模型的可解释性也可以泛化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-site validation of an interpretable model to analyze breast masses.

An external validation of IAIA-BL-a deep-learning based, inherently interpretable breast lesion malignancy prediction model-was performed on two patient populations: 207 women ages 31 to 96, (425 mammograms) from iCAD, and 58 women (104 mammograms) from Emory University. This is the first external validation of an inherently interpretable, deep learning-based lesion classification model. IAIA-BL and black-box baseline models had lower mass margin classification performance on the external datasets than the internal dataset as measured by AUC. These losses correlated with a smaller reduction in malignancy classification performance, though AUC 95% confidence intervals overlapped for all sites. However, interpretability, as measured by model activation on relevant portions of the lesion, was maintained across all populations. Together, these results show that model interpretability can generalize even when performance does not.

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来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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