通过多模态目标级对比学习提升癌症生存风险预测精度
A Multimodal Object-level Contrast Learning Method for Cancer Survival Risk Prediction
- 基于样本生存风险关系构建对比对，实现目标级对比学习
- 在两个公开数据集上超越现有最优方法，显著提升预测性能
- 适合医学影像与基因组数据融合分析的研究者参考
基于计算机的癌症生存风险预测在患者及时治疗中具有重要意义。这是一个涉及病理图像、基因组数据等多临床因素的弱监督序数回归挑战任务。本文提出一种新的训练方法——多模态目标级对比学习，用于癌症生存风险预测。首先，根据训练集中样本间的生存风险关系构建对比学习对；随后引入目标级对比学习方法训练生存风险预测器。进一步通过跨模态对比拓展至多模态场景。针对病理图像与基因组数据的异质性，分别采用基于注意力机制和自归一化神经网络构建多模态生存风险预测器。最终，由所提方法训练的预测器在两个公开多模态癌症数据集上均优于当前最优方法。
原文摘要 · Abstract (English)
Computer-aided cancer survival risk prediction plays an important role in the timely treatment of patients. This is a challenging weakly supervised ordinal regression task associated with multiple clinical factors involved such as pathological images, genomic data and etc. In this paper, we propose a new training method, multimodal object-level contrast learning, for cancer survival risk prediction. First, we construct contrast learning pairs based on the survival risk relationship among the samples in the training sample set. Then we introduce the object-level contrast learning method to train the survival risk predictor. We further extend it to the multimodal scenario by applying cross-modal constrast. Considering the heterogeneity of pathological images and genomics data, we construct a multimodal survival risk predictor employing attention-based and self-normalizing based nerural network respectively. Finally, the survival risk predictor trained by our proposed method outperforms state-of-the-art methods on two public multimodal cancer datasets for survival risk prediction.
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