Deep Data Source Fusion with Bias-Undoing for Lung Adenocarcinoma Classification

Abstract

Computer-aided diagnosis of early-stage lung adenocarcinoma based on deep learning is prospective for assisting the prevention and treatment of the deathly disease of lung cancer, however, relevant works face the problem of limited training data. The technique of data source fusion with the training of deep models on multiple relevant datasets is promising to resolve the lack of training data, while the bias of data distribution from different data sources exists as a universal issue to affect the learning performance. In this paper, we propose a deep learning framework based on bias-undoing data source fusion to classify early stages of lung adenocarcinoma in computed tomography (CT) images. The framework conducts learning on the integrated datasets for respectively natural image, lung nodule CT and lung adenocarcinoma CT, as designed with an organization of base parameters and bias parameters to adapt to the data distribution with bias. Experimental results demonstrate that the proposed bias-undoing framework is effective to improve the performance of deep learning for lung adenocarcinoma classification, and is with great superiority to those general fusion frameworks on alleviating the effect of dataset bias.