Work

Cross-domain convergence of generative models: From biomedical to astronomical applications

Year: 2026

Type: article

Abstract: This paper investigates the convergence of generative modeling techniques across diverse image analysis tasks by examining their application in two data-intensive scientific domains: biomedical imaging and astronomy. In these two domains, which tend to be scientifically distinct due to their size and aims, they share common challenges, including noise corruption, limited availability of annotated data, and the demand for high-fidelity image reconstruction. This study provides a critical review of the various variants of generative models, with a particular focus on cross-domain applications. Unlike existing surveys that predominantly focus on a single discipline, this study emphasises the transferability and adaptability of generative models across biomedical and astronomical imaging. The proposed review highlights the potential offered by generative models, particularly Generative Adversarial Networks (GANS), in enhancing data generation, image restoration, and analysis in both biomedical and astronomical studies. • Presents a comprehensive study on the convergence of GAN models across biomedical and astronomical domains. • Provides a comparative analysis of model capacity to handle domain- specific challenges. • Proposes future research pathways for leveraging cross-domain convergence in scientific discovery.

Source: Machine Learning with Applications

DOI: 10.1016/j.mlwa.2026.100841

Authors: Hajer Ghodhbani, Suvendi Rimer, Khmaies Ouahada, Adel M. Alimi

Institutions: University of Johannesburg, University of Sfax

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,340

Funder: University of Johannesburg