A Multi-view Feature Decomposition Deep Learning Method for Lung Cancer Histology Classification

Accurate classification of squamous cell carcinoma (SCC) and adenocarcinoma (ADC) using computed tomography (CT) images is of great significance to guide treatment for patients with non-small cell lung cancer (NSCLC). Although existing deep learning methods have made promising progress in this area, they do not fully exploit tumor information to learn discriminative representations. In this study, we propose a multi-view feature decomposition deep learning method for lung cancer histology classification. Different from existing multi-view methods that directly fuse features extracted from different views, we propose a feature decomposition module (FDM) to decompose the features of axial, coronal and sagittal views into common and specific features through an attention mechanism. To constrain this feature decomposition, a feature similarity loss is introduced to make common features obtained from different views to be similar to each other. Moreover, to assure the effectiveness of feature decomposition, we design a cross-reconstruction loss which enforces each view to be reconstructed according to its own specific feature and other view's common features. After the above feature decomposition, comprehensive representations of tumors can be obtained by efficiently integrating common features to improve the classification performance. Experimental results demonstrate that our method outperforms other state-of-the-art methods.