Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks
This work addresses data scarcity in medical imaging for researchers and practitioners, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of small medical datasets by generating photorealistic red blood cell images and segmentation masks using conditional generative adversarial networks, which are then used to augment training data for segmentation and object detection tasks, with effectiveness quantified through experiments on a manually collected dataset.
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.