@techreport{oai:ipsj.ixsq.nii.ac.jp:00075149,
 author = {Taiju, Inagaki and Hayaru, Shouno and Shoji, Kido and Taiju, Inagaki and Hayaru, Shouno and Shoji, Kido},
 issue = {2},
 month = {Jul},
 note = {We propose a computer aided diagnosis (CAD) system for classification of idiopathic interstitial pneumonias (IIPs). High resolution computed tomography (HRCT) images are considered as effective for diagnosis of IIPs. Our proposed CAD system is based on the convolutional-net that is bio-plausible neural network model inspired from the visual system such like human. The convolutional-net extract local features and integrate them in the process of hierarchical neural network system. For natural image recognition by convolutionalnet, Gabor feature extraction is known to give a good performance, however, the HRCT images may have different properties from those of natural images. Thus, we introduce a learning type feature extraction called “sparse coding” into the convolutional-net, and evaluate performance for classification of IIPs., We propose a computer aided diagnosis (CAD) system for classification of idiopathic interstitial pneumonias (IIPs). High resolution computed tomography (HRCT) images are considered as effective for diagnosis of IIPs. Our proposed CAD system is based on the convolutional-net that is bio-plausible neural network model inspired from the visual system such like human. The convolutional-net extract local features and integrate them in the process of hierarchical neural network system. For natural image recognition by convolutionalnet, Gabor feature extraction is known to give a good performance, however, the HRCT images may have different properties from those of natural images. Thus, we introduce a learning type feature extraction called “sparse coding” into the convolutional-net, and evaluate performance for classification of IIPs.},
 title = {Classification of Idiopathic Interstitial Pneumonia CT Images using Convolutional-net with Sparse Feature Extractors},
 year = {2011}
}