A Multimodal Object-level Contrast Learning Method for Cancer Survival Risk Prediction
This work addresses a weakly supervised ordinal regression problem for cancer patients, potentially improving timely treatment decisions.
The paper tackled cancer survival risk prediction by proposing a multimodal object-level contrast learning method, which outperformed state-of-the-art methods on two public datasets.
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.