Abstract | ||
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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. |
Year | DOI | Venue |
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2019 | 10.1117/12.2512746 | Proceedings of SPIE |
Keywords | DocType | Volume |
interstitial pneumonia,ILD,weakly-supervised,representation learning,neural network | Conference | 10950 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Chenglong Wang | 1 | 5 | 4.13 |
Takayasu Moriya | 2 | 3 | 2.42 |
Yuichiro Hayashi | 3 | 147 | 24.97 |
Holger R Roth | 4 | 156 | 9.20 |
Le Lu | 5 | 1297 | 86.78 |
Masahiro Oda | 6 | 182 | 40.81 |
Hirotugu Ohkubo | 7 | 0 | 0.34 |
Kensaku Mori | 8 | 1125 | 160.28 |