講演抄録/キーワード |
講演名 |
2018-09-21 10:00
[ショートペーパー]Computer-Aided Diagnosis of Liver Cancers Using Deep Learning with Fine-tuning ○Weibin Wang(Ritsumeikan Univ.)・Dong Liang・Lanfen Lin・Hongjie Hu・Qiaowei Zhang・Qingqing Chen(Zhejiang Univ.)・Yutaro lwamoto・Xianhua Han・Yen-Wei Chen(Ritsumeikan Univ.) PRMU2018-57 IBISML2018-34 |
抄録 |
(和) |
(まだ登録されていません) |
(英) |
Liver cancer is one of the leading causes of death world-wide. Computer-aided diagnosis plays an important role in liver lesion diagnosis (classification). Recently, several deep learning-based computer-aided diagnosis systems have been proposed for classification of liver lesions and their effectiveness have been demonstrated. The main challenge in deep learning-based medical image classification is the lack of annotated training samples. In this paper, we demonstrated that fine-tuning can significantly improve the liver lesion classification accuracy especially for the small training samples. We used the residual convolutional neural network (ResNet), which is the state-of-the-art network, as our baseline network for focal liver lesion classification on multi-phase CT images. The fine-tuning significantly improved the classification accuracy from 83.7% to 91.2%. The classification accuracy (91.2%) is higher than the accuracy of the state-of-the-art methods. |
キーワード |
(和) |
/ / / / / / / |
(英) |
ResNet / Liver cancer classification / Multi-phase CT / Fine-tuning / / / / |
文献情報 |
信学技報, vol. 118, no. 219, PRMU2018-57, pp. 139-140, 2018年9月. |
資料番号 |
PRMU2018-57 |
発行日 |
2018-09-13 (PRMU, IBISML) |
ISSN |
Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
PRMU2018-57 IBISML2018-34 |