发表机构
Helmholtz Munich; Technical University of Munich; King’s College London; Munich Center for Machine Learning（亥姆霍兹慕尼黑; 慕尼黑工业大学; 伦敦国王学院; 慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于提示的多模态框架TACTIC，整合全身MRI与结构化临床数据，可处理缺失临床数据，在五项WB-MRI分类任务中优于仅图像方法，提升诊断性能。
AI 中文摘要
将全身磁共振成像（WB-MRI）与临床变量相结合，有望通过利用患者信息的互补来源改善系统性疾病诊断。然而，结构化临床变量往往不完整或缺失，限制了假设输入固定的传统多模态融合方法的适用性。本研究提出TACTIC（用于图像分类的表格属性条件Transformer），这是一种基于提示的多模态框架，通过条件视觉特征学习整合WB-MRI和结构化临床数据。通过将临床属性编码为提示，TACTIC支持任意数量的表格输入，且无需插补或固定输入结构即可自然处理缺失数据。我们在涵盖系统性和肿瘤学应用的五项WB-MRI分类任务上评估TACTIC，包括糖尿病、慢性阻塞性肺疾病（COPD）、乳腺癌、前列腺癌和转移灶诊断。在所有任务中，当临床信息可用时，TACTIC始终比仅图像基线方法提升性能，同时在表格输入不完整时保持强大的预测能力。我们的结果证明了基于提示的模型作为利用临床上下文改善WB-MRI分析的灵活方法的有效性。模型权重和代码可在该https URL获取。
英文摘要
Combining whole-body magnetic resonance imaging (WB-MRI) with clinical variables has the potential to improve systemic disease diagnosis by leveraging complementary sources of patient information. However, structured clinical variables are often incomplete or missing, limiting the applicability of conventional multimodal fusion methods that assume fixed inputs. In this work, we propose TACTIC (Tabular-Attribute Conditioned Transformer for Image Classification), a prompt-based multimodal framework that integrates WB-MRI and structured clinical data through conditional visual feature learning. By encoding clinical attributes as prompts, TACTIC supports an arbitrary number of tabular inputs and naturally handles missing data without requiring imputation or fixed input structures. We evaluate TACTIC on five WB-MRI classification tasks spanning systemic and oncologic applications, including diabetes, chronic obstructive pulmonary disease (COPD), breast cancer, prostate cancer, and metastasis diagnosis. Across all tasks, TACTIC consistently improves performance over image-only baselines when clinical information is available while maintaining strong predictive capability under incomplete tabular inputs. Our results demonstrate the effectiveness of prompt-based models as a flexible approach for improving WB-MRI analysis using clinical context. The model weights and code are available at https://github.com/lauradaza/TACTIC
