WEKO3
-
RootNode
アイテム
Classification of Idiopathic Interstitial Pneumonia CT Images using Convolutional-net with Sparse Feature Extractors
https://ipsj.ixsq.nii.ac.jp/records/75149
https://ipsj.ixsq.nii.ac.jp/records/75149bceb2b95-08c8-4f19-bf38-ffdf4baabdd3
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-MPS11084002.pdf (514.7 kB)
|
Copyright (c) 2011 by the Information Processing Society of Japan
|
|
| オープンアクセス | ||
| Item type | SIG Technical Reports(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2011-07-11 | |||||||
| タイトル | ||||||||
| タイトル | Classification of Idiopathic Interstitial Pneumonia CT Images using Convolutional-net with Sparse Feature Extractors | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | Classification of Idiopathic Interstitial Pneumonia CT Images using Convolutional-net with Sparse Feature Extractors | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||
| 資源タイプ | technical report | |||||||
| 著者所属 | ||||||||
| Graduate School of Informatics and Engineering, University of Electro-Communications | ||||||||
| 著者所属 | ||||||||
| Graduate School of Informatics and Engineering, University of Electro-Communications | ||||||||
| 著者所属 | ||||||||
| Graduate School of Medicine, Yamaguchi Univeristy | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Graduate School of Informatics and Engineering, University of Electro-Communications | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Graduate School of Informatics and Engineering, University of Electro-Communications | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Graduate School of Medicine, Yamaguchi Univeristy | ||||||||
| 著者名 |
Taiju, Inagaki
Hayaru, Shouno
Shoji, Kido
× Taiju, Inagaki Hayaru, Shouno Shoji, Kido
|
|||||||
| 著者名(英) |
Taiju, Inagaki
Hayaru, Shouno
Shoji, Kido
× Taiju, Inagaki Hayaru, Shouno Shoji, Kido
|
|||||||
| 論文抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 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. | |||||||
| 論文抄録(英) | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 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. | |||||||
| 書誌レコードID | ||||||||
| 収録物識別子タイプ | NCID | |||||||
| 収録物識別子 | AN10505667 | |||||||
| 書誌情報 |
研究報告数理モデル化と問題解決（MPS） 巻 2011-MPS-84, 号 2, p. 1-6, 発行日 2011-07-11 |
|||||||
| Notice | ||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||
| 出版者 | ||||||||
| 言語 | ja | |||||||
| 出版者 | 情報処理学会 | |||||||
