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Automatic Metastatic Bone Tumor Classification with DCNN-based Features Using Treatment-planning CT Images
會議論文集

Automatic Metastatic Bone Tumor Classification with DCNN-based Features Using Treatment-planning CT Images

Haruna Watanabe, Ren Togo, Takahiro Ogawa, Miki Haseyama, Koichi Yasuda, Khin Khin Tha, Kohsuke Kudo, Hiroki Shirato 和 Fang-Hsin Chen
Proceedings of SPIE, the international society for optical engineering, 卷.11050, 110501
Proceedings of SPIE
01/01/2019
Web of Science ID: WOS:000468223800044

摘要

Imaging Science & Photographic Technology Optics Physical Sciences Science & Technology Technology
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.

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