Classification for Rectal CEUS Images Based on Combining Features by Transfer Learning
Authors:
Open Access
- 24 August 2019
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 187-191
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (81570848)
- Science and Technology Department of Sichuan Province (2019YFS0126)
This publication has 13 references indexed in Scilit:
- A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid UltrasoundIEEE Journal of Biomedical and Health Informatics, 2016
- A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast‐enhanced ultrasoundMedical Physics, 2015
- Deep learning based classification of focal liver lesions with contrast-enhanced ultrasoundOptik, 2014
- Double–Contrast‐Enhanced Sonography for Diagnosis of Rectal Lesions With Pathologic CorrelationJournal of Ultrasound in Medicine, 2014
- Features of time–intensity curve parameters of colorectal adenocarcinomas evaluated by double-contrast enhanced ultrasonography: Initial observationEuropean Journal of Radiology, 2012
- Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kineticsJournal of Vacuum Science & Technology B, 2012
- Comments and Illustrations Regarding the Guidelines and Good Clinical Practice Recommendations for Contrast-Enhanced Ultrasound (CEUS) – Update 2008Ultraschall in der Medizin - European Journal of Ultrasound, 2008
- Guidelines and Good Clinical Practice Recommendations for Contrast Enhanced Ultrasound (CEUS) - Update 2008Ultraschall in der Medizin - European Journal of Ultrasound, 2008
- An introduction to ROC analysisPattern Recognition Letters, 2006
- Characterization of Focal Liver Lesions with Contrast-specific US Modes and a Sulfur Hexafluoride–filled Microbubble Contrast Agent: Diagnostic Performance and ConfidenceRadiology, 2004