An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features; and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low-and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium(LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches. Keywords: Lung nodule classification, lung cancer diagnosis, Computed tomography, deep learning, convolutional neural networks, model interpretability 1. Introduction and Background Lung cancer is the leading cause of cancer mortality worldwide [1, 2]. Computed tomography (CT) imaging is widely used to detect pulmonary nodules and forms the basis for diagnosing lung cancer. Based on the findings of the NLST, the United States Preventative Services Task Force (USPSTF) went on to recommend low-dose CT lung cancer screening for current and former smokers aged 55-80 with a smoking history of at least 30 pack-years, or former smokers having quit within the past 15 years [4]. However, the potential consequences of implementing lung cancer screening is an increase in false positive screens that result in unnecessary medical, economic, and psychological costs.