English(EN) Explainable Multimodal Deep Learning Integrating Imaging and Clinical Data for Oral Potentially Malignant Disorder Detection

新的多模态AI框架利用影像学和临床数据改进口腔癌检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了M2-OPMDNet，一个新颖的多模态深度学习框架，旨在改进口腔潜在恶性疾病（OPMDs）的检测。该系统整合了包括白光和自发荧光在内的影像学数据，以及结构化的临床信息，如患者风险因素和症状。该框架展示了0.952的高AUC，优于单模态方法，并通过SHapley Additive exPlanations（SHAP）提供可解释性，以阐明特征和模态的贡献。M2-OPMDNet为现实世界的口腔癌筛查和临床决策支持提供了一个可扩展的解决方案。 AI

影响增强了口腔潜在恶性疾病的早期检测，可能改善患者预后并降低医疗成本。

排序理由详细介绍用于医学诊断的新型多模态深度学习框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Ruilin You, Yihan Wang, Jiabin Chen, Cherie Wink, Petra Wilder-Smith, Rongguang Liang, Bofan Song · 2026-09-07 04:00

面向口腔潜在癌变疾病检测的集成影像学和临床数据的可解释多模态深度学习

arXiv:2609.04512v1 Announce Type: cross Abstract: Oral potentially malignant disorders (OPMDs) are critical precursors to oral cancer, yet clinical detection remains challenging because of substantial phenotypic heterogeneity and overlap with benign conditions. Although image-bas…

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面向口腔潜在癌变疾病检测的集成影像学和临床数据的可解释多模态深度学习

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