Multimodal Prototyping for cancer survival prediction

• URL: http://arxiv.org/abs/2407.00224v1
• Date: Fri, 28 Jun 2024 20:37:01 GMT
• Title: Multimodal Prototyping for cancer survival prediction
• Authors: Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood,
• Abstract summary: Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification. Current approaches involve tokenizing the WSIs into smaller patches (>10,000 patches) and transcriptomics into gene groups, which are then integrated using a Transformer for predicting outcomes. This process generates many tokens, which leads to high memory requirements for computing attention and complicates post-hoc interpretability analyses. Our framework outperforms state-of-the-art methods with much less computation while unlocking new interpretability analyses.
• Score: 45.61869793509184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification. Current approaches involve tokenizing the WSIs into smaller patches (>10,000 patches) and transcriptomics into gene groups, which are then integrated using a Transformer for predicting outcomes. However, this process generates many tokens, which leads to high memory requirements for computing attention and complicates post-hoc interpretability analyses. Instead, we hypothesize that we can: (1) effectively summarize the morphological content of a WSI by condensing its constituting tokens using morphological prototypes, achieving more than 300x compression; and (2) accurately characterize cellular functions by encoding the transcriptomic profile with biological pathway prototypes, all in an unsupervised fashion. The resulting multimodal tokens are then processed by a fusion network, either with a Transformer or an optimal transport cross-alignment, which now operates with a small and fixed number of tokens without approximations. Extensive evaluation on six cancer types shows that our framework outperforms state-of-the-art methods with much less computation while unlocking new interpretability analyses.

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