Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images - Collections

＜Doctoral Thesis＞
Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images

	Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images
Creator	Creator Name 永見, 範幸 NAGAMI, Noriyuki ナガミ, ノリユキ
Examiner	佐々木, 雅之
	杜下, 淳次
	藤淵, 俊王
Language	English
Academic Year Conferred	2022
Conferring University	九州大学
Conferring University	Kyushu University
Degree	DOCTOR OF PHILOSOPHY IN HEALTH SCIENCES
Degree Type	Doctoral Degree
Publication Type	Accepted Manuscript
Access Rights	open access
Related DOI	https://doi.org/10.1007/s13246-022-01202-7
Related URI	https://rdcu.be/dhegF

File	FileType	Size	Views	Description
med3685	pdf	697 KB	254	本文(著者版)
med3685_abstract	pdf	132 KB	117	要旨
med3685_review	pdf	113 KB	124	審査結果要旨

Record ID	6787494
Peer-Reviewed	Refereed
Rights	やむを得ない事由により本文ファイル非公開 (2)
Rights	Public access to the fulltext file is restricted for unavoidable reason (2)
Related PubMed ID	36469246
Report Number	17102甲第16464号
Number of Diploma	医博甲第3685号
Granted Date	2023-03-20
Date Accepted	2023-01-26
Faculty	医博
Created Date	2023.05.16
Modified Date	2023.07.24