End-To-End Chromosome Karyotyping with Data Augmentation Using GAN

Classifying human chromosomes from input cell images, i.e., karyotyping, requires domain expertise and quantity of manual effort to perform. In this paper, we propose an end-to-end chromosome karyotyping method, which can automatically detect, segment and classify chromosomes from cell images. During detection, we explore Extremal Regions (ER) to obtain chromosome candidates in input images. During segmentation, we segment overlapping chromosome candidates by approximating chromosome shapes with eclipses. In classification, we first propose Multiple Distribution Generative Advertising Network (MD-GAN) to effectively cover diverse data modes and generate more labeled samples for data augmentation. Then, we finetune pre-trained convolutional neural network (CNN) to classify chromosomes with samples generated by MD-GAN. We demonstrate the accuracy of the proposed end-to-end method in detecting, segmenting and classifying by experiments on a self-collected dataset. Experiments also prove data augmentation with MD-GAN could improve classification performance of CNN.