Masamichi Yagi
Overview
Explore the profile of Masamichi Yagi including associated specialties, affiliations and a list of published articles.
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Articles
5
Citations
47
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0
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Recent Articles
1.
Nemoto T, Futakami N, Kunieda E, Yagi M, Takeda A, Akiba T, et al.
Igaku Butsuri
. 2023 Apr;
43(1):19.
PMID: 37045760
No abstract available.
2.
Nemoto T, Takeda A, Matsuo Y, Kishi N, Eriguchi T, Kunieda E, et al.
JCO Clin Cancer Inform
. 2022 Jun;
6:e2100176.
PMID: 35749675
Purpose: Clear evidence indicating whether surgery or stereotactic body radiation therapy (SBRT) is best for non-small-cell lung cancer (NSCLC) is lacking. SBRT has many advantages. We used artificial neural networks...
3.
Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs
Nemoto T, Futakami N, Kunieda E, Yagi M, Takeda A, Akiba T, et al.
Radiol Phys Technol
. 2021 Jul;
14(3):318-327.
PMID: 34254251
Deep learning has demonstrated high efficacy for automatic segmentation in contour delineation, which is crucial in radiation therapy planning. However, the collection, labeling, and management of medical imaging data can...
4.
Nemoto T, Futakami N, Yagi M, Kunieda E, Akiba T, Takeda A, et al.
Phys Med
. 2020 Sep;
78:93-100.
PMID: 32950833
Purpose: Deep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management of medical imaging data, because of ethical complications and the...
5.
Nemoto T, Futakami N, Yagi M, Kumabe A, Takeda A, Kunieda E, et al.
J Radiat Res
. 2020 Feb;
61(2):257-264.
PMID: 32043528
This study aimed to examine the efficacy of semantic segmentation implemented by deep learning and to confirm whether this method is more effective than a commercially dominant auto-segmentation tool with...