Leveraging Vision Transformers and Diffusion Models for Medical Imaging Diagnosis: A Study on Weakly Supervised and Zero-Shot Learning for Rare Diseases

Author(s)	Mr. Rajeshwar Kadari, Mr. Praveen Kumar Valaboju
Country	India
Abstract	The emergence of Vision Transformers (ViTs) and diffusion models has transformed the landscape of computer vision, particularly in medical imaging diagnosis. This research examines the utilization of ViTs and diffusion models for diagnosing conditions through X-rays, MRI, and CT scans, specifically emphasizing rare diseases. It investigates the effectiveness of weakly supervised and zero-shot learning approaches to tackle the challenges associated with the limited availability of annotated data for uncommon pathologies. The study introduces an innovative hybrid architecture combining ViTs and diffusion models, thereby improving accuracy and generalization. Experimental findings indicate enhanced diagnostic capabilities, a decreased dependence on extensive labelled datasets, and promising potential for application in clinical environments.
Keywords	Vision Transformers, Diffusion Models, Medical Imaging, Weakly Supervised Learning, Zero-Shot Learning, Rare Diseases, X-rays, MRI, CT, Deep Learning
Field	Computer Applications
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-28
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.45572
Short DOI	https://doi.org/g9mnxf

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International Journal For Multidisciplinary Research