Transfer learning for classification of cardiovascular tissues in histological images.

Journal: Computer methods and programs in biomedicine

PMID: 30337082

Abstract

BACKGROUND AND OBJECTIVE: Automatic classification of healthy tissues and organs based on histology images is an open problem, mainly due to the lack of automated tools. Solutions in this regard have potential in educational medicine and medical practices. Some preliminary advances have been made using image processing techniques and classical supervised learning. Due to the breakthrough performance of deep learning in various areas, we present an approach to recognise and classify, automatically, fundamental tissues and organs using Convolutional Neural Networks (CNN).

Authors

Claudia Mazo

University of Valle, Computer and Systems Engineering School, Cali, Colombia. Electronic address: claudia.mazo@correounivalle.edu.co.
Jose Bernal

Institute of Computer Vision and Robotics, University of Girona, Ed. P-IV, Campus Montilivi, Girona, 17003, Spain. Electronic address: jose.bernal@udg.edu.
Maria Trujillo

University of Valle, Computer and Systems Engineering School, Cali, Colombia.
Enrique Alegre

University of León, Industrial and Informatics Engineering School, León, Spain.

Keywords

Algorithms Cardiovascular System Deep Learning Histological Techniques Humans Image Processing, Computer-Assisted Machine Learning Models, Anatomic Models, Cardiovascular Neural Networks, Computer Reference Values Support Vector Machine

External Resources

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Transfer learning for classification of cardiovascular tissues in histological images.

Abstract

Authors

Keywords

External Resources

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