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Proceedings Paper

Automatic metastatic bone tumor classification with DCNN-based features using treatment-planning CT
Author(s): Haruna Watanabe; Ren Togo; Takahiro Ogawa; Miki Haseyama; Koichi Yasuda; Khin Khin Tha; Kohsuke Kudo; Hiroki Shirato
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Paper Abstract

In this paper, we propose a method to classify metastatic bone tumors using treatment-planning computed tomography images. The proposed method utilizes pre-trained deep convolutional neural network (DCNN) models as feature extractors and enables the metastatic bone tumor classification by using the obtained features. Performance of several state-of-the-art DCNN-based features was compared and evaluated in our experiment.

Paper Details

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501A (27 March 2019); doi: 10.1117/12.2521406
Show Author Affiliations
Haruna Watanabe, Hokkaido Univ. (Japan)
Ren Togo, Hokkaido Univ. (Japan)
Takahiro Ogawa, Hokkaido Univ. (Japan)
Miki Haseyama, Hokkaido Univ. (Japan)
Koichi Yasuda, Hokkaido Univ. (Japan)
Khin Khin Tha, Hokkaido Univ. Hospital (Japan)
Kohsuke Kudo, Hokkaido Univ. Hospital (Japan)
Hiroki Shirato, Hokkaido Univ. Hospital (Japan)

Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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