Combined Data Augmentation for HEp-2 Cells Image Classification

The Antinuclear Antibody (ANA) test is a valuable diagnostic tool for autoimmune disorders that uses Indirect Immunofluorescence (IIF) microscopy with HEp-2 cells as the substrate to identify antibodies and their distinct staining patterns. Machine learning-based approaches have shown promise in automating this diagnosis process, with Data Augmentation (DA) techniques playing a crucial role in improving performance. Even though traditional DA methods have yielded positive results, generative techniques like Variational AutoEncoders (VAEs) have shown potential in exploring the input distribution and generating new images. To address the limitations of traditional DA and explore the potential of generative approaches, this paper focuses on applying Conditional Variational AutoEncoders (CVAEs) to HEp-2 cell image classification. A customized CVAE architecture is proposed, considering multiple labels during generation to enhance versatility. Extensive experiments were conducted with the largest publicly available dataset of HEp-2 cell images, the I3A dataset. The performance of traditional and generative data augmentation techniques were compared while investigating potential synergies between them. The findings highlight the benefits of combining these techniques, especially in scenarios with class imbalance. Thorough statistical analysis provides valuable insights from the experimental results.