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Conference papers

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 : geodesic operators Convolutional neural network Mathematical morphology Histological image segmentation
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Submitted on : Thursday, September 20, 2018 - 10:19:39 AM
Last modification on : Wednesday, November 17, 2021 - 12:27:15 PM
Long-term archiving on: : Friday, December 21, 2018 - 1:57:18 PM

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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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