MIST模型通过基因组引导的病理注意力实现多模态生存预测
MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention
这个研究提出了一个叫MIST的新方法，用基因组信息来指导病理图像的分析，在预测癌症患者生存期方面效果更好。
MIST模型通过将基因组特征表示为token，并允许它们查询来自病理学基础模型的紧凑上下文token，从而在生存预测前融合分子信息与病理学上下文。
这种设计在训练时结合了离散时间生存预测、基因组特征掩码、WSI dropout和配对WSI-基因组对比对齐。在结肠癌、肾癌、肺癌和胶质母细胞瘤四个外部队列的评估中，MIST在主要比较中显著提升了外部C-index。
MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention
Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity. To address these challenges, we propose MIST, multimodal survival prediction with genomic-guided histology attention. MIST represents genomic features as tokens and allows them to query compact foundation-model-derived histology context tokens before survival prediction. This design enriches molecular information with histology context rather than merging separately encoded modalities only at the final stage. Training combines discrete-time survival prediction with genomic feature masking, WSI dropout, and paired WSI-genomics contrastive alignment. Across four external evaluations in colon, renal, lung, and glioblastoma cohorts, MIST improves external C-index over standard fusion baselines in the primary comparisons. These results support genomic-guided histology attention as a compact and effective strategy for multimodal oncology outcome prediction. Our code is available at https://github.com/samiyavuuz/MIST .
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：MIST、多模态、病理学、生存预测，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Muhammet Sami Yavuz, Sabri Mustafa Kahya, Richard R. Chen, Jana Lipkova, Benedikt Wiestler），经 AITOP 多维评分管线结构化提炼。