Augmentation method for convolutional neural network that improves prediction performance in the task of classifying primary lung cancer and lung metastasis using CT images

Takuma Usuzaki; Kengo Takahashi; Kazuma Umemiya; Mami Ishikuro; Taku Obara; Masahiro Kamimoto; Kazuhiro Majima

doi:10.1016/j.lungcan.2021.06.021

Augmentation method for convolutional neural network that improves prediction performance in the task of classifying primary lung cancer and lung metastasis using CT images

Takuma Usuzaki, Kengo Takahashi, Kazuma Umemiya, Mami Ishikuro, Taku Obara, Masahiro Kamimoto, Kazuhiro Majima

Tohoku Medical Megabank Organization (ToMMo)

Research output: Contribution to journal › Letter › peer-review

5 Citations (Scopus)

Original language	English
Pages (from-to)	175-178
Number of pages	4
Journal	Lung Cancer
Volume	160
DOIs	https://doi.org/10.1016/j.lungcan.2021.06.021
Publication status	Published - 2021 Oct

Keywords

Deep learning
Lung cancer
Lung nodule

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.lungcan.2021.06.021

Cite this

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title = "Augmentation method for convolutional neural network that improves prediction performance in the task of classifying primary lung cancer and lung metastasis using CT images",

keywords = "Deep learning, Lung cancer, Lung nodule",

author = "Takuma Usuzaki and Kengo Takahashi and Kazuma Umemiya and Mami Ishikuro and Taku Obara and Masahiro Kamimoto and Kazuhiro Majima",

note = "Funding Information: The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI database used in this study. Data used in this research were obtained from The Cancer Imaging Archive (TCIA) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH, https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.",

year = "2021",

month = oct,

doi = "10.1016/j.lungcan.2021.06.021",

language = "English",

volume = "160",

pages = "175--178",

journal = "Lung Cancer",

issn = "0169-5002",

publisher = "Elsevier Ireland Ltd",

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TY - JOUR

T1 - Augmentation method for convolutional neural network that improves prediction performance in the task of classifying primary lung cancer and lung metastasis using CT images

AU - Usuzaki, Takuma

AU - Takahashi, Kengo

AU - Umemiya, Kazuma

AU - Ishikuro, Mami

AU - Obara, Taku

AU - Kamimoto, Masahiro

AU - Majima, Kazuhiro

N1 - Funding Information: The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI database used in this study. Data used in this research were obtained from The Cancer Imaging Archive (TCIA) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH, https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.

PY - 2021/10

Y1 - 2021/10

KW - Deep learning

KW - Lung cancer

KW - Lung nodule

UR - http://www.scopus.com/inward/record.url?scp=85111528437&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85111528437&partnerID=8YFLogxK

U2 - 10.1016/j.lungcan.2021.06.021

DO - 10.1016/j.lungcan.2021.06.021

M3 - Letter

C2 - 34303540

AN - SCOPUS:85111528437

SN - 0169-5002

VL - 160

SP - 175

EP - 178

JO - Lung Cancer

JF - Lung Cancer

ER -

Augmentation method for convolutional neural network that improves prediction performance in the task of classifying primary lung cancer and lung metastasis using CT images

Keywords

UN SDGs

Access to Document

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