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Presentation + Paper
13 March 2019 Weakly-supervised deep learning of interstitial lung disease types on CT images
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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.
Conference Presentation
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Chenglong Wang, Takayasu Moriya, Yuichiro Hayashi, Holger Roth, Le Lu, Masahiro Oda, Hirotugu Ohkubo, and Kensaku Mori "Weakly-supervised deep learning of interstitial lung disease types on CT images", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501H (13 March 2019);

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