ISING-GAN: Annotated Data Augmentation with a Spatially Constrained Generative Adversarial Network
Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou
Abstract
Data augmentation is a popular technique with which new dataset samples are artificially synthesized to the end of assisting training of learning-based algorithms and avoiding overfitting. Methods based on generative adversarial networks (GANs) have recently rekindled interest in research on new techniques for data augmentation. With the current paper we propose a new GAN-based model for data augmentation, comprising a suitable Markov random field-based spatial constraint that encourages synthesis of spatially smooth outputs. Oriented towards use with medical imaging sets where a localization/segmentation annotation is available, our model can simultaneously also produce artificial annotations. We gauge performance numerically by measuring performance through U-Net trained to detect cells on microscopy images, by taking into account the produced augmented dataset. Numerical trials, as well as qualitative results validate the contribution of our model.