HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic Samples.

The paper examines the possibilities of using synthetic HEp-2 cell images as a means of data augmentation. The common problem of biomedical datasets is the shortage of annotated samples required for the training of deep learning techniques. Traditional approaches based on image rotation and mirroring have their limitations, and alternative techniques based on generative adversarial networks (GANs) are currently being explored. Instead of looking solely at a single dataset or the creation of a recognition model with applicability for multiple datasets, this study focuses on the transferability of synthetic HEp-2 samples among publicly available datasets. The paper offers a workflow where the quality of synthetic samples is confirmed via an independent fine-tuned neural network. The subsequent combination of synthetic samples with original images outperforms traditional augmentation approaches and leads to state-of-the-art performance on both publicly available HEp-2 cell image datasets employed in this study.