Weakly-supervised Deep Learning of Interstitial Lung Disease Types on CT Images

Accurate classification and precise quantification of interstitial lung disease (ILD) types on CT images remain important challenges in clinical diagnosis. Multi-modality image information is required to assist diagnosing diseases. To build scalable deep-learning solutions for this problem, how to take full advantage of existing large-scale datasets in modern hospitals has become a critical task. In this paper, we present DeepILD, as a novel computer-aided diagnostic framework to address the ILD classification task only from single modality (CT image) using a deep neural network. More specifically, we propose integrating spherical semi-supervised K-means clustering and convolutional neural networks for ILD classification and disease quantification. We firstly use semi-supervised spherical K-means to divide the CT lung area into normal and abnormal sub-regions. A convolutional neural network (CNN) is subsequently invoked to perform training using image patches extracted from the abnormal regions. Here, we focus on the classification of three chronic fibrosing ILD types: idiopathic pulmonary fibrosis (IPF), idiopathic non-specific interstitial pneumonia (iNSIP), and chronic hypersensitivity pneumonia (CHP). Excellent classification accuracy has been achieved using a dataset of 188 CT scans; in particular, our IPF classification reached about 88% accuracy.