Jo Ozaki, Tomoyuki Fujioka, Emi Yamaga, Atsushi Hayashi, Y. Kujiraoka, Tomoki Imokawa, Kanae Takahashi, Sayuri Okawa, Yuka Yashima, Mio Mori, Kazunori Kubota, Goshi Oda, Tsuyoshi Nakagawa, U. Tateishi
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Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography
期刊介绍:
Japanese Journal of Radiology is a peer-reviewed journal, officially published by the Japan Radiological Society. The main purpose of the journal is to provide a forum for the publication of papers documenting recent advances and new developments in the field of radiology in medicine and biology. The scope of Japanese Journal of Radiology encompasses but is not restricted to diagnostic radiology, interventional radiology, radiation oncology, nuclear medicine, radiation physics, and radiation biology. Additionally, the journal covers technical and industrial innovations. The journal welcomes original articles, technical notes, review articles, pictorial essays and letters to the editor. The journal also provides announcements from the boards and the committees of the society. Membership in the Japan Radiological Society is not a prerequisite for submission. Contributions are welcomed from all parts of the world.