A Context Aware Lung Cancer Survival Prediction Network by Using Whole Slide Images

Xinyu Liu, Yicheng Wang, Ye Luo

Abstract: Lung cancer has caused enormous harm to human life and traditional whole slide image (WSI) based lung cancer survival prediction methods suffer from information loss and can not maintain the spatial context of the images, which may play the important roles into survival analysis. Meanwhile, the impact of the heterogeneity between the medical images and the natural images has been noticed for some pre-trained models on medical image representation learning. In this paper, we proposed a Context Aware Lung Cancer Survival Prediction Network (CA-SurvNet) by using the whole slide images, in which the survival prediction is decided by every patch of a WSI and its associated spatial context as well. Specifically, the representation of every WSI patch is first learned via a self-supervised learning based feature extractor, and then are sequentially concatenated followed by a channel-wisely dimensional reduction to preserve the significant information and maintain the spatial structure of the WSI simultaneously. Extensive experiments on two large benchmark datasets validate the superiority of the proposed method to its state-of-the-art competitors, and also its effectiveness of the WSI context preserving into the lung cancer survival prediction.