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Authors: Rune Wetteland 1 ; Kjersti Engan 1 ; Trygve Eftestøl 1 ; Vebjørn Kvikstad 2 and Emilius Janssen 3

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

ISBN: 978-989-758-351-3

Keyword(s): Histological Whole-Slide Images, Autoencoder, Deep Learning, Semi-supervised Learning, ROI Extraction.

Abstract: 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 dimension al 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. (More)

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Paper citation in several formats:
Wetteland, R.; Engan, K.; Eftestøl, T.; Kvikstad, V. and Janssen, E. (2019). Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 320-327. DOI: 10.5220/0007253603200327

@conference{icpram19,
author={Rune Wetteland. and Kjersti Engan. and Trygve Eftestøl. and Vebjørn Kvikstad. and Emilius A. M. Janssen.},
title={Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={320-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007253603200327},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks
SN - 978-989-758-351-3
AU - Wetteland, R.
AU - Engan, K.
AU - Eftestøl, T.
AU - Kvikstad, V.
AU - Janssen, E.
PY - 2019
SP - 320
EP - 327
DO - 10.5220/0007253603200327

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