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http://hdl.handle.net/10609/91330
Title: Applying deep learning/GANs to histology image for data augmentation: a general study
Author: Martinez Garcia, Juan Pablo
Director: Vegas Lozano, Esteban
Keywords: data augmentation
GAN
deep learning
histology
Issue Date: Jan-2018
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: In medical imaging tasks, annotations are made by radiologists with expert knowledge on the data and task. Therefore, Histology images are especially difficult to collect as they are: expensive, time consuming and information can not be always disclosed for research. To tackle all these issues data augmentation is a popular solution. Data augmentation, consist of generating new training samples from existing ones, boosting the size of the dataset. When applying any type of artificial neural network, the size of the training is key factor to be successful. especially when employing supervised machine learning algorithms that require labelled data and large training examples. We present a method for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated histology images can be used for synthetic data augmentation and improve the performance of CNN for medical image classification. The GAN is a non-supervised machine learning technique where one network generates candidates (generative) and the other evaluates them (discriminative) to generate new sample like the original. In our case we will focus in a type of GAN called Deep Convolution Generative Convolutional Network (DCGAN) where the CNN architecture is used in both networks and the discriminator is reverting the process created by the generator. Finally, we will apply this technique, for data augmentation, with two different datasets: Narrow bone and Breast tissue histology image. To check the result, we will classify the synthetic images with a pre-trained CNN with real images and labelled by specialist.
Language: English
URI: http://hdl.handle.net/10609/91330
Appears in Collections:Bachelor thesis, research projects, etc.

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