Early esophageal adenocarcinoma detection using deep learning methods.

Journal: International journal of computer assisted radiology and surgery

Published Date: Jan 22, 2019

Abstract

PURPOSE: This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) images.

Authors

Noha Ghatwary

University of Lincoln, Lincoln, UK. nghatwary@lincoln.ac.uk.
Massoud Zolgharni

University of Lincoln, Lincoln, UK.
Xujiong Ye

School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK. Electronic address: xye@lincoln.ac.uk.

Keywords

Adenocarcinoma Deep Learning Early Diagnosis Esophageal Neoplasms Humans Neural Networks, Computer Reproducibility of Results

External Resources

View on PubMed Access via DOI PubMed (30666547)

Early esophageal adenocarcinoma detection using deep learning methods.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals