A progressive phased attention model fused histopathology image features and gene features for lung cancer staging prediction.

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Identifying the stage of lung cancer accurately from histopathology images and gene is very important for the diagnosis and treatment of lung cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to large intra-class variances, and a high degree of inter-class similarities. In this paper, we propose a phased Multimodal Multi-scale Attention Model (MMAM) that predicts lung cancer stages using histopathology image data and gene data. The model consists of two phases. In Phase1, we propose a Staining Difference Elimination Network (SDEN) to eliminate staining differences between different histopathology images, In Phase2, it utilizes the image feature extractor provided by Phase1 to extract image features, and sends the multi-scale image features together with gene features into our Adaptive Enhanced Attention Fusion (AEAF) module for multimodal multi-scale features fusion to enable prediction of lung cancer staging. We evaluated the proposed MMAM on the TCGA lung cancer dataset, and achieved 88.51% AUC and 88.17% accuracy on classification prediction of lung cancer stages I, II, III, and IV. The method can help doctors diagnose the stage of lung cancer patients and can benefit from multimodal data.