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Authors: Ilias Rmouque 1 ; Maxime Devanne 2 ; Jonathan Weber 2 ; Germain Forestier 2 and Cédric Wemmert 1

Affiliations: 1 ICube, University of Strasbourg, France ; 2 IRIMAS, University of Haute-Alsace, France

Keyword(s): Digital Pathology, Deep Learning, Autoencoders, Histopathology, Ensemble Learning, Segmentation.

Abstract: Unsupervised deep learning using autoencoders has shown excellent results in image analysis and computer vision. However, only few studies have been presented in the field of digital pathology, where proper labelling of the objects of interest is a particularly costly and difficult task. Thus, having a first fully unsupervised segmentation could greatly help in the analysis process of such images. In this paper, many architectures of convolutional autoencoders have been compared to study the influence of three main hyperparameters: (1) number of convolutional layers, (2) number of convolutions in each layer and (3) size of the latent space. Different clustering algorithms are also compared and we propose a new way to obtain more precise results by applying ensemble clustering techniques which consists in combining multiple clustering results.

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Paper citation in several formats:
Rmouque, I.; Devanne, M.; Weber, J.; Forestier, G. and Wemmert, C. (2022). Ensemble Clustering for Histopathological Images Segmentation using Convolutional Autoencoders. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 933-940. DOI: 10.5220/0010835300003124

@conference{visapp22,
author={Ilias Rmouque. and Maxime Devanne. and Jonathan Weber. and Germain Forestier. and Cédric Wemmert.},
title={Ensemble Clustering for Histopathological Images Segmentation using Convolutional Autoencoders},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={933-940},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010835300003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Ensemble Clustering for Histopathological Images Segmentation using Convolutional Autoencoders
SN - 978-989-758-555-5
IS - 2184-4321
AU - Rmouque, I.
AU - Devanne, M.
AU - Weber, J.
AU - Forestier, G.
AU - Wemmert, C.
PY - 2022
SP - 933
EP - 940
DO - 10.5220/0010835300003124
PB - SciTePress

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