arXiv cs.AI· Maryam Baizhigitova, Andrew Seohwan Yu, Po-Hao Chen, Naveen Subhas, Sixu Chen, Xinxin Wang, Kunio Nakamura, Richard Lartey, Xiaojuan Li, Mingrui Yang·· 10 天前AI 评分26
Knee3DVLM：双序列全容积视觉语言建模用于全面膝关节 MRI 评估
Knee3DVLM: Dual-Sequence Full-Volume Vision-Language Modeling for Comprehensive Knee MRI Assessment
AI 导读
Knee3DVLM 是一个序列感知的视觉语言模型，利用全容积 DESS 和流体敏感 TSE MRI 预测源自 MOAKS 的 57 个解剖定位二分类诊断目标。在 1,074 例检查的留出队列中，融合模型取得 72.98% 平均准确率、71.17% 平衡准确率、78.96% 平均 ROC-AUC 和 78.74% 宏 ROC-AUC，为三种配置中最高。
正文 · 原文
Abstract:Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited, particularly for interpreting the complementary sequences used in clinical practice. We introduce Knee3DVLM, a sequence-aware VLM that uses full-volume DESS and fluid-sensitive TSE MRI to predict 57 anatomically resolved binary diagnostic targets derived from the MRI Osteoarthritis Knee Score (MOAKS) for structured reporting. We evaluated DESS-only, TSE-only, and paired DESS-TSE configurations using subject-disjoint Osteoarthritis Initiative partitions. In a held-out cohort of 1,074 examinations, the fused model achieved 72.98% average accuracy, 71.17% balanced accuracy, 78.96% mean ROC-AUC, and 78.74% macro ROC-AUC, the highest values among the three configurations. In a secondary multiclass analysis aligned with the released 3DReasonKnee cohort, Knee3DVLM was numerically higher than the strongest reported 3DReasonKnee configuration across five pathology categories. These findings support dual-sequence full-volume modeling for comprehensive knee MRI assessment.
| Comments: | 11 pages, 2 figures, 5 tables |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08482 [cs.CV] |
| (or arXiv:2610.08482v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08482 arXiv-issued DOI via DataCite |
Submission history
From: Maryam Baizhigitova [view email]
[v1]
Tue, 6 Oct 2026 14:58:53 UTC (895 KB)
[v2]
Wed, 7 Oct 2026 02:15:47 UTC (895 KB)
来源：arXiv cs.AI · arxiv.org