LoV3D：通过区域体积评估在纵向3D脑MRI中实现认知预后推理
LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments
AI总结：
LoV3D通过纵向3D脑MRI的区域体积评估，实现认知预后推理，其核心方法是结合3D视觉-语言模型，通过区域解剖评估、纵向对比和诊断输出，提高诊断准确性与泛化能力。
摘要：
纵向脑MRI对于表征神经疾病如阿尔茨海默病的进展至关重要。然而，当前深度学习工具将此过程碎片化：分类器将扫描简化为标签，体积管线产生无法解释的测量，而视觉-语言模型（VLMs）可能生成流畅但可能产生幻觉的结论。我们提出了LoV3D，一个训练3D视觉-语言模型的流程，该流程读取纵向T1加权脑MRI，生成区域级解剖评估，与先前扫描进行纵向比较，并最终输出三类诊断（认知正常、轻度认知障碍或痴呆）以及合成诊断总结。该分步流程通过强制标签一致性、纵向连贯性和生物学合理性来实现最终诊断，从而减少幻觉风险。训练过程引入了临床加权的验证器，该验证器自动评分候选输出，与标准化体积指标派生的规范参考进行比较，驱动直接偏好优化，而无需单个人类注释。在受试者层面的ADNI测试集（479个扫描，258个受试者）上，LoV3D实现了93.7%的三类诊断准确率（比无基础基线高34.8%），97.2%的二类诊断准确率（比SOTA高4%），以及82.6%的区域级解剖分类准确率（比VLM基线高33.1%）。零样本迁移在MIRIAD上达到95.4%（100%痴呆召回率），在AIBL上达到82.9%的三类准确率，证实了在不同地点、扫描仪和人群中的高泛化能力。代码可在https://github.com/Anonymous-TEVC/LoV-3D上获得。
英文摘要：
Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer's disease assessment. However, current deep-learning tools fragment this process: classifiers reduce a scan to a label, volumetric pipelines produce uninterpreted measurements, and vision-language models (VLMs) may generate fluent but potentially hallucinated conclusions. We present LoV3D, a pipeline for training 3D vision-language models, which reads longitudinal T1-weighted brain MRI, produces a region-level anatomical assessment, conducts longitudinal comparison with the prior scan, and finally outputs a three-class diagnosis (Cognitively Normal, Mild Cognitive Impairment, or Dementia) along with a synthesized diagnostic summary. The stepped pipeline grounds the final diagnosis by enforcing label consistency, longitudinal coherence, and biological plausibility, thereby reducing the risks of hallucinations. The training process introduces a clinically-weighted Verifier that scores candidate outputs automatically against normative references derived from standardized volume metrics, driving Direct Preference Optimization without a single human annotation. On a subject-level held-out ADNI test set (479 scans, 258 subjects), LoV3D achieves 93.7% three-class diagnostic accuracy (+34.8% over the no-grounding baseline), 97.2% on two-class diagnosis accuracy (+4% over the SOTA) and 82.6% region-level anatomical classification accuracy (+33.1% over VLM baselines). Zero-shot transfer yields 95.4% on MIRIAD (100% Dementia recall) and 82.9% three-class accuracy on AIBL, confirming high generalizability across sites, scanners, and populations. Code is available at https://github.com/Anonymous-TEVC/LoV-3D.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
