Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-...

Full description

Saved in:

Bibliographic Details
Published in:	Cancers Vol. 16; no. 10; p. 1810
Main Authors:	Umemoto, Mina, Mariya, Tasuku, Nambu, Yuta, Nagata, Mai, Horimai, Toshihiro, Sugita, Shintaro, Kanaseki, Takayuki, Takenaka, Yuka, Shinkai, Shota, Matsuura, Motoki, Iwasaki, Masahiro, Hirohashi, Yoshihiko, Hasegawa, Tadashi, Torigoe, Toshihiko, Fujino, Yuichi, Saito, Tsuyoshi
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.05.2024
Subjects:	Algorithms Artificial intelligence Cancer Classification Cloning Colon cancer Data mining Deep learning Diagnosis Digitization Endometrial cancer Endometrium Genomes Ligands Machine learning Medical imaging equipment Methods Mismatch repair Monoclonal antibodies MSH6 protein Neural networks Oncology, Experimental Prognosis Proteins Tumors Uterine cancer Yeast Japan deep learning digital pathology endometrial cancer whole-slide imaging mismatch repair biomarker artificial intelligence molecular classification
ISSN:	2072-6694, 2072-6694
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!