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Department of Pathology, Stavanger University Hospital, Norway, Department of Mathematics and Natural Sciences, University of Stavanger and Norway
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1 Papers

Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks

Topics: Medical Imaging; Industry Related Applications; Deep Learning and Neural Networks; Classification and Clustering

Authors: Rune Wetteland 1 , Kjersti Engan 1 , Trygve Eftestøl 1 , Vebjørn Kvikstad 2 and Emilius A. M. Janssen 3

Affiliations: 1 Department of Electrical Engineering and Computer Science, University of Stavanger and Norway , 2 Department of Pathology, Stavanger University Hospital and Norway , 3 Department of Pathology, Stavanger University Hospital, Norway, Department of Mathematics and Natural Sciences, University of Stavanger and Norway

Digital Object Identifier: 10.5220/0007253603200327

Proceedings of ICPRAM 2019, ISBN: 978-989-758-351-3

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Globally there has been an enormous increase in bladder cancer incidents the past decades. Correct prognosis of recurrence and progression is essential to avoid under- or over-treatment of the patient, as well as unnecessary suffering and cost. To diagnose the cancer grade and stage, pathologists study the histological images. However, this is a time-consuming process and reproducibility among pathologists is low. A first stage for an automated diagnosis system can be to identify the diagnostical relevant areas in the histological whole-slide images (WSI), segmenting cell tissue from damaged areas, blood, background, etc. In this work, a method for automatic classification of urothelial carcinoma into six different classes is proposed. The method is based on convolutional neural networks (CNN), firstly trained unsupervised using unlabelled images by utilising an autoencoder (AE). A smaller set of labelled images are used to train the final fully-connected layers from the low dimensional latent vector of the AE, providing an output as a probability score for each of the six classes, suitable for automatically defining regions of interests in WSI. For evaluation, each tile is classified as the class with the highest probability score. The model achieved an average F1-score of 93.4% over all six classes.
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Pattern Recognition

Software Engineering
Medical Imaging

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