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Generative adversarial networks: a renewal for data augmentation in lung nodule classification

Obtaining medical images is an ethically restrictive process and still difﬁcult to validate, depending on well-trained professional, being a laborious and time-consuming activity. Therefore, the construction of large databases of structured medical images is one of the major challenges of the deep learning applications in the computerized aid to the diagnosis in medical images. GAN presents itself as an adequate solution to supply the small number of pathological exams that compose the most diverse medical images banks. In this work we intend to develop a method using GAN to balance the data set of Computed Tomography images and improve the performance of an arbitrary classiﬁer of pulmonary nodules. For this, two GAN architectures with the capacity to generate synthetic images of nodal cuts were trained and in a second moment, a Convolutional Neural Network was trained in rooms of different data set. The training of the different data sets were evaluated by AUC-ROC.