REMEMBER：基于检索的可解释多模态证据引导建模在零样本和少样本神经退行性疾病诊断中的脑评估与推理
REMEMBER: Retrieval-based Explainable Multimodal Evidence-guided Modeling for Brain Evaluation and Reasoning in Zero- and Few-shot Neurodegenerative Diagnosis
AI总结：
针对神经退行性疾病诊断中深度学习模型依赖大规模标注数据、缺乏可解释性及临床数据规模小/无标注的问题，提出REMEMBER框架，通过检索式多模态证据引导实现零样本和少样本诊断，兼具可解释性与稳健性能。
摘要：
阿尔茨海默病等神经退行性疾病的及时准确诊断是疾病管理的核心。现有深度学习模型需要大规模标注数据集，且常作为“黑箱”运行；临床实践中的数据集往往规模小或无标注，限制了深度学习方法的潜力。本文提出REMEMBER——基于检索的可解释多模态证据引导脑评估与推理建模，这是一种通过基于参考的推理过程实现零样本和少样本阿尔茨海默病诊断的机器学习框架。具体而言，REMEMBER首先利用专家标注的参考数据训练对比对齐的视觉-文本模型，并扩展编码异常类型、诊断标签和复合临床描述的伪文本模态；推理时，从 curated 数据集检索相似的人类验证案例，通过专用证据编码模块和基于注意力的推理头整合其上下文信息。这种证据引导设计使REMEMBER能通过将预测基于检索的影像和文本上下文，模仿真实临床决策过程；其输出诊断预测及包含参考图像和符合临床工作流程解释的可解释报告。实验结果表明，REMEMBER在零样本和少样本场景下表现稳健，为现实世界中基于神经影像的诊断（尤其数据有限时）提供了强大且可解释的框架。
英文摘要：
Timely and accurate diagnosis of neurodegenerative disorders, such as Alzheimer's disease, is central to disease management. Existing deep learning models require large-scale annotated datasets and often function as "black boxes". Additionally, datasets in clinical practice are frequently small or unlabeled, restricting the full potential of deep learning methods. Here, we introduce REMEMBER -- Retrieval-based Explainable Multimodal Evidence-guided Modeling for Brain Evaluation and Reasoning -- a new machine learning framework that facilitates zero- and few-shot Alzheimer's diagnosis using brain MRI scans through a reference-based reasoning process. Specifically, REMEMBER first trains a contrastively aligned vision-text model using expert-annotated reference data and extends pseudo-text modalities that encode abnormality types, diagnosis labels, and composite clinical descriptions. Then, at inference time, REMEMBER retrieves similar, human-validated cases from a curated dataset and integrates their contextual information through a dedicated evidence encoding module and attention-based inference head. Such an evidence-guided design enables REMEMBER to imitate real-world clinical decision-making process by grounding predictions in retrieved imaging and textual context. Specifically, REMEMBER outputs diagnostic predictions alongside an interpretable report, including reference images and explanations aligned with clinical workflows. Experimental results demonstrate that REMEMBER achieves robust zero- and few-shot performance and offers a powerful and explainable framework to neuroimaging-based diagnosis in the real world, especially under limited data.
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。
