Generating Lymphoma Variability Through Adversarial Generation Models: A Limited Resource Approach for Synthetic Histopathology Image Generation and Classification

Abstract

Lymphomas, a diverse group of blood cancers originating from the lymphatic system, present significant challenges for diagnosis and treatment due to their morphological and genetic differences. These variability obscures the development of personalized therapies and impacts disease outcomes. The scarcity of high-quality, diverse images of lymphoma cells is further hindered by patient privacy concerns, the rarity of certain types, and logistical challenges of collecting and processing biomedical images. Additionally, the costs and technical difficulties of microscopic imaging limit the scope of research. This paper addresses these challenges by utilizing generative adversarial networks (GANs) to generate synthetic yet realistic images of lymphoma histopathology and classify them in a low resource setting. Focusing specifically on chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), we employed GANs to enhance the understanding of lymphoma variability and support advancements in digital cancer biology and pathology. This dual approach of image generation and classification aims to improve disease diagnosis and prognosis, ultimately contributing to better patient outcomes.

Note:
Funding: This research was funded by the National Science and Technology Council, Taiwan (NSTC 113-2113-M038-001) and Taipei Medical University.

Conflict of Interests: None.

Keywords: GAN, Cancer Pathology, Digital Biology, Lymphoma, MCL, CLL