Transfer learning for classification of cardiovascular tissues in histological images. (October 2018)

• Record Type: Journal Article
• Authors: Mazo, Claudia; Bernal, Jose; Trujillo, Maria; Alegre, Enrique
• Is Part Of: Computer methods and programs in biomedicine. Volume 165(2018)
• Page Start: 69
• Page End: 76
• Publication Date: 2018-10
• Subjects: Transfer learning -- SVM -- Fundamental tissues -- Organs -- Cardiovascular system -- Histological images; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.08.006
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We present a CNN to classify automatically fundamental tissues and organs. We adapt different CNNs to our domain through transfer learning. Our strategy yielded outstanding results compared to the state of the art. A public version of our code has been released at our research website. 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). Methods: We adapt four popular CNNs architectures – ResNet, VGG19, VGG16 and Inception – to this problem through transfer learning. The resulting models are evaluated at three stages. Firstly, all the transferred networks are compared to each other. Secondly, the best resulting fine-tuned model is compared to an ad-hoc 2D multi-path model to outline the importance of transfer learning. Thirdly, the same model is evaluated against the state-of-the-art method, a cascade SVM using LBP-based descriptors, to contrast a traditional machine learning approach and a representation learning one. The evaluation task consists of separating six classes accurately: smooth muscle of the elastic artery, smooth muscle of the large vein, smooth muscle of the muscular artery, cardiac muscle, loose connective tissue, and light regions. The different networks are tuned on 6000 blocks of 100 × 100 pixels and tested on 7500. Results: Our proposal yields F-score values between 0.717 and 0.928. The highest and lowest performances are for cardiac muscle and smooth muscle of the large vein, respectively. The main issue leading to limited classification scores for the latter class is its similarity with the elastic artery. However, this confusion is evidenced during manual annotation as well. Our algorithm reached improvements in F-score between 0.080 and 0.220 compared to the state-of-the-art machine learning approach. Conclusions: We conclude that it is possible to classify healthy cardiovascular tissues and organs automatically using CNNs and that deep learning holds great promise to improve tissue and organs classification. We left our training and test sets, models and source code publicly available to the research community.