2019/01/18 by Oleksandr Bailo, Bailo, Oleksandr, DongShik Ham +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #cs.CV
paper · pdf · doi:10.48550/arxiv.1901.06219
openalex publication_date 2019/01/18 · arxiv created 2019/03/08 · arxiv updated 2019/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we describe how to apply image-to-image translation techniques to medical blood smear data to generate new data samples and meaningfully increase small datasets. Specifically, given the segmentation mask of the microscopy image, we are able to generate photorealistic images of blood cells which are further used alongside real data during the network training for segmentation and object detection tasks. This image data generation approach is based on conditional generative adversarial networks which have proven capabilities to high-quality image synthesis. In addition to synthesizing blood images, we synthesize segmentation mask as well which leads to a diverse variety of generated samples. The effectiveness of the technique is thoroughly analyzed and quantified through a number of experiments on a manually collected and annotated dataset of blood smear taken under a microscope.