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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 .
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Contributor : Etienne Decencière Connect in order to contact the contributor
Submitted on : Friday, June 28, 2019 - 11:49:56 AM
Last modification on : Wednesday, November 17, 2021 - 12:27:16 PM


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  • HAL Id : hal-02167903, version 1


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⟩



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