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Transformer-Based Multimodal Fusion for Survival Prediction by Integrating Whole Slide Images, Clinical, and Genomic Data.

Yihang Chen,Weiqin Zhao,Lequan Yu

2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI(2023)

Renmin Univ China | Univ Hong Kong

Cited 0|Views20
Abstract
Survival prediction using whole slide images (WSIs) is a complex and difficult task, as handling gigapixel WSI directly is computationally impossible. In the past few years, people have worked out multiple instance learning (MIL) strategies to deal with WSIs, i.e., splitting WSI into many patches (instances) and aggregating features across patches. Moreover, to better predict the survival outcome of patients, different modalities have been explored, among which gene features are used the most frequently. In this paper, we explore a graph-based strategy to handle WSIs and investigate a transformer-based strategy to combine different modalities for survival prediction. Moreover, clinical data was also adopted and different encoding manners of clinical information were explored. Experiments on two public datasets from The Cancer Genome Atlas (TCGA) demonstrate the effectiveness of the proposed graph-transformer framework for survival prediction.
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Survival Prediction,Whole Slide Image,Multi-modality,Transformer,Graph Neural Network
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要点】:本文提出了一种基于图和变换器(Transformer)的多模态融合方法,用于整合全玻片图像(WSI)、临床和基因组数据以预测患者生存率,实现了对多源异构数据的有效融合。

方法】:作者采用图策略处理WSI,并将变换器模型应用于多模态数据的融合,同时探索了不同编码方式处理临床信息。

实验】:实验在两个来自癌症基因组图谱(TCGA)的公开数据集上进行,结果表明提出的图-变换器框架在生存预测方面的有效性。