Ensemble clustering for histopathological images segmentation using convolutional autoencoders

Ilias Rmouque (1) , Jonathan Weber (2) , Maxime Devanne (2) , Germain Forestier (2) , Cédric Wemmert (1)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 IRIMAS - Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499

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.

Domaines: Apprentissage [cs.LG]

Ilias Rmouque, Jonathan Weber, Maxime Devanne, Germain Forestier, Cédric Wemmert. Ensemble clustering for histopathological images segmentation using convolutional autoencoders. International Conference on Computer Vision Theory and Applications (VISAPP), Feb 2022, En ligne, France. pp.933-940, ⟨10.5220/0010835300003124⟩. ⟨hal-03469780⟩