Ensemble Clustering for Histopathological Images Segmentation using Convolutional Autoencoders

Ilias Rmouque, Maxime Devanne, Jonathan Weber, Germain Forestier, Cédric Wemmert

2022

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 933-940. DOI: 10.5220/0010835300003124

in Bibtex Style

@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},
}

in EndNote Style

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
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