ADVANCED SELF-CONDITIONED GAN FOR HISTOLOGY IMAGE SYNTHESIS AND DATA AUGMENTATION

Abstract

The article explores modern methods and innovative approaches aimed at advancing medical image generation through the application of deep generative models, with particular emphasis on state-of-the-art Generative Adversarial Network (GAN) architectures enhanced by self-conditioning mechanisms in scenarios of limited data availability. A central research objective is the synthesis of histological images that combine high visual fidelity, strong realism, and adequate variability, thereby making them suitable for both clinical practice and scientific research applications. By generating artificial yet realistic images, the proposed methodology contributes to overcoming one of the most pressing barriers in medical imaging research—namely, the scarcity of sufficiently large and diverse annotated datasets.

The paper provides an in-depth analysis of the main challenges inherent in medical image synthesis. These include the constraints imposed by limited and heterogeneous training datasets, the difficulty of ensuring anatomical and structural consistency in generated outputs, and the problem of maintaining stability during adversarial training. To evaluate the proposed solution, experiments were conducted using the PathMNIST dataset, a benchmark collection of histopathological image sections widely applied in computational pathology.

The experimental results clearly demonstrate the benefits of incorporating self-conditioning within GAN frameworks. Specifically, self-conditioning was shown to stabilize the adversarial training process, significantly mitigate the risk of mode collapse, and improve the overall perceptual quality of generated samples. Furthermore, improvements were confirmed quantitatively through objective image quality metrics as well as classifier performance when trained on augmented data. These findings underscore the potential of the proposed approach for practical applications in data augmentation, robust evaluation of diagnostic algorithms, and the development of decision-support systems in digital pathology.

The contribution of this work lies not only in the methodological novelty of applying self-conditioning to medical image generation, but also in its practical relevance for clinical AI pipelines, where high-quality synthetic data can accelerate innovation while ensuring reproducibility and generalizability of diagnostic models.