Dealing with Topological Information within a Fully Convolutional Neural Network

Etienne Decencière 1 Santiago Velasco-Forero 1 Fu Min Juanjuan Chen Hélène Burdin Gervais Gauthier 2 Bruno Lay 3 Thomas Bornschloegl Thérèse Baldeweck 4
1 CMM - Centre de Morphologie Mathématique
2 ADCIS - Société ADCIS
3 ADCIS - Société ADCIS
4 L'Oréal recherche et innovation
Abstract : A fully convolutional neural network has a receptive field of limited size and therefore cannot exploit global information, such as topological information. A solution is proposed in this paper to solve this problem, based on pre-processing with a geodesic operator. It is applied to the segmentation of histological images of pigmented reconstructed epidermis acquired via Whole Slide Imaging.
Keywords : Mathematical morphology Histological image segmentation Convolutional neural network geodesic operators
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Communication dans un congrès
Advanced Concepts for Intelligent Vision Systems (ACIVS), Sep 2018, Poitiers, France
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01877624
Contributeur : Etienne Decencière <>
Soumis le : jeudi 20 septembre 2018 - 10:19:39
Dernière modification le : lundi 12 novembre 2018 - 10:58:12

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Etienne Decencière, Santiago Velasco-Forero, Fu Min, Juanjuan Chen, Hélène Burdin, et al.. Dealing with Topological Information within a Fully Convolutional Neural Network. Advanced Concepts for Intelligent Vision Systems (ACIVS), Sep 2018, Poitiers, France. 〈hal-01877624〉

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