文献
J-GLOBAL ID:202202222297084254
整理番号:22A0747094
PET/CTイメージングによる肺癌病変セグメンテーションのための少数ショットU-Net深層学習モデル【JST・京大機械翻訳】
A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging
著者 (9件):
Protonotarios Nicholas E
(Department of Applied Mathematics & Theoretical Physics, University of Cambridge, United Kingdom)
,
Katsamenis Iason
(School of Rural & Surveying Engineering, National Technical University of Athens, Greece)
,
Sykiotis Stavros
(School of Rural & Surveying Engineering, National Technical University of Athens, Greece)
,
Dikaios Nikolaos
(Mathematics Research Center, Academy of Athens, Greece)
,
Kastis George A
(Mathematics Research Center, Academy of Athens, Greece)
,
Chatziioannou Sofia N
(PET/CT Department, Biomedical Research Foundation Academy of Athens, Greece)
,
Metaxas Marinos
(PET/CT Department, Biomedical Research Foundation Academy of Athens, Greece)
,
Doulamis Nikolaos
(School of Rural & Surveying Engineering, National Technical University of Athens, Greece)
,
Doulamis Anastasios
(School of Rural & Surveying Engineering, National Technical University of Athens, Greece)
資料名:
Biomedical Physics & Engineering Express
(Biomedical Physics & Engineering Express)
巻:
8
号:
2
ページ:
025019 (12pp)
発行年:
2022年
JST資料番号:
W5552A
ISSN:
2057-1976
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
イギリス (GBR)
言語:
英語 (EN)