@mastersthesis {xGullon18,
	title = {Retinal lesions segmentation using CNNs and adversarial training},
	year = {2018},
	abstract = {<p><span style="color: $\#$333333; font-family: {\textquoteright}Helvetica Neue{\textquoteright}, Helvetica, Arial, sans-serif; font-size: 13px; text-align: justify;">Convolutional Neural Networks (CNNs) are frequently used to tackle image classification and segmentation problems due to its recently proven successful results. In particular, in medical domain, it is more and more common to see automated techniques to help doctors in their diagnosis. In this work, we study the retinal lesions segmentation problem using CNNs on the Indian Diabetic Retinopathy Image Dataset (IDRiD). Additionally, the idea of adversarial training used by Generative Adversarial Networks (GANs) will be also added to the previous CNN to improve its results, making segmentation maps more accurate and realistic. A comparison between these two architectures will be made. One of the main challenges we will be facing is the high-imbalance between lesions and healthy parts of the retina and the fact that some lesion classes are very scattered in small fractions. Thus, different loss functions, optimizers and training schemes will be studied and evaluated to see which one best addresses our problem.</span></p>},
	author = {Nat{\`a}lia Gull{\'o}n},
	editor = {Ver{\'o}nica Vilaplana}
}
