A NEW COLOR AUGMENTATION METHOD FOR DEEP LEARNING SEGMENTATION OF HISTOLOGICAL IMAGES

Abstract : This paper addresses the problem of labeled data insufficiency in neural network training for semantic segmentation of color-stained histological images acquired via Whole Slide Imaging. It proposes an efficient image augmentation method to alleviate the demand for a large amount of labeled data and improve the network's generalization capacity. Typical image augmentation in bioimaging involves geometric transformation. Here, we propose a new image augmentation technique by combining the structure of one image with the color appearance of another image to construct augmented images on-the-fly for each training iteration. We show that it improves performance in the segmentation of histological images of human skin, and also offers better results when combined with geometric transformation .
Liste complète des métadonnées

Littérature citée [30 références]  Voir  Masquer  Télécharger

https://hal-mines-paristech.archives-ouvertes.fr/hal-02167903
Contributeur : Etienne Decencière <>
Soumis le : vendredi 28 juin 2019 - 11:49:56
Dernière modification le : dimanche 7 juillet 2019 - 01:41:16

Fichier

Paper_v2.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-02167903, version 1

Citation

Yang Xiao, Etienne Decencière, Santiago Velasco-Forero, Hélène Burdin, Thomas Bornschlögl, et al.. A NEW COLOR AUGMENTATION METHOD FOR DEEP LEARNING SEGMENTATION OF HISTOLOGICAL IMAGES. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), Apr 2019, Venise, France. ⟨hal-02167903⟩

Partager

Métriques

Consultations de la notice

200

Téléchargements de fichiers

117