Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining

• URL: http://arxiv.org/abs/2505.12711v2
• Date: Tue, 20 May 2025 12:57:58 GMT
• Title: Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining
• Authors: Qichen Sun, Zhengrui Guo, Rui Peng, Hao Chen, Jinzhuo Wang,
• Abstract summary: ALTER is a tri-modal pretraining framework that integrates WSIs, genomics, and pathology reports.<n>It learns robust, cross-modal representations beyond WSI-centric approaches.<n>We evaluate ALTER across extensive clinical tasks including survival prediction, cancer subtyping, gene mutation prediction, and report generation.
• Score: 7.22968366818898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in computational pathology and artificial intelligence have significantly enhanced the utilization of gigapixel whole-slide images and and additional modalities (e.g., genomics) for pathological diagnosis. Although deep learning has demonstrated strong potential in pathology, several key challenges persist: (1) fusing heterogeneous data types requires sophisticated strategies beyond simple concatenation due to high computational costs; (2) common scenarios of missing modalities necessitate flexible strategies that allow the model to learn robustly in the absence of certain modalities; (3) the downstream tasks in CPath are diverse, ranging from unimodal to multimodal, cnecessitating a unified model capable of handling all modalities. To address these challenges, we propose ALTER, an any-to-any tri-modal pretraining framework that integrates WSIs, genomics, and pathology reports. The term "any" emphasizes ALTER's modality-adaptive design, enabling flexible pretraining with any subset of modalities, and its capacity to learn robust, cross-modal representations beyond WSI-centric approaches. We evaluate ALTER across extensive clinical tasks including survival prediction, cancer subtyping, gene mutation prediction, and report generation, achieving superior or comparable performance to state-of-the-art baselines.

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