A Robust Framework for Stress Testing and Reconstruction in Medical Imaging Systems

Hospitals have saved digitalized ECG reports and medical reports for many years to better treat their patients.The electrocardiogram (ECG) scans have shown diversity and relationship with the patient’s previous medical record. The electrocardiogram (ECG) scans exhibit considerable diversity and correlate with the patient’s medical history making it as an important parameter of research. The initial scanned ECG is noisy, so it cannot be used to train the data models and develop appropriate diagnosis algorithms. Therefore, it is necessary to preprocess it. To get around this problem, this research suggests an unsupervised synthetic ECG picture producing technique. The study, which will be introduced in a robust framework designed to stress test medical imaging models, has the sole purpose of focusing on the ECG images used (electrocardiograms) to diagnose diseases. This framework aims to find how robust these models are in real-world variation by applying gradual perturbations and advanced image reconstruction techniques. In order to apply a wide variety of realistic perturbations, the study makes use of Deep Convolutional Generative Adversarial Networks (DCGANs). This helps in enhancing data sets and ensuring that the models are thoroughly tested. The study identifies defects within models and proposes several strategies to improvetheir performance which include architectural improvements and implementation of adversarial training, through which model performance can be improved. The results of this work include a standardized validation framework, in-depth performance analyses, and a certified prototype tested on real ECG datasets. This guarantees that the models not only perform well in known environments but also show better generalizability and clinical reliability in unknown real-world applications, specifically in disease diagnosis and monitoring cardiac health after surgery.