Classify and Explain: An Interpretable Convolutional Neural Network For Lung Cancer Diagnosis

The deep network-based computer-aided diagnosis systems have encountered many difficulties in practical applications because of its "black box" feature. The crux of the problem is that these models should be explainable - the model should provide doctors rationales that can explain the diagnosis. In this paper, we present a novel network structure for visually interpretable lung nodule diagnosis. Our proposed model works in an end-to-end manner, consisting of an importance estimation network and a classification network. The former produces a diagnostic visual interpretation for each case, and the latter diagnoses the case. Based on a computed tomography image dataset (LUNA16) on pulmonary nodule, extensive experiments have been conducted, demonstrating that the proposed model can produce state-of-the-art diagnostic visual interpretations compared with all baseline methods.