Applying Conditional Generative Adversarial Networks for Imaging Diagnosis

TL;DR

This paper presents a novel deep learning approach combining Conditional GANs with Stacked Hourglass Networks to improve medical image segmentation, addressing overfitting and achieving superior results in IVUS imaging.

Contribution

It introduces a hybrid loss function and data augmentation techniques within a C-GAN framework for enhanced medical image segmentation without heavy domain knowledge.

Findings

Superior segmentation accuracy on IVUS images

Effective overfitting mitigation through data augmentation

Enhanced diagnostic potential in medical imaging

Abstract

This study introduces an innovative application of Conditional Generative Adversarial Networks (C-GAN) integrated with Stacked Hourglass Networks (SHGN) aimed at enhancing image segmentation, particularly in the challenging environment of medical imaging. We address the problem of overfitting, common in deep learning models applied to complex imaging datasets, by augmenting data through rotation and scaling. A hybrid loss function combining L1 and L2 reconstruction losses, enriched with adversarial training, is introduced to refine segmentation processes in intravascular ultrasound (IVUS) imaging. Our approach is unique in its capacity to accurately delineate distinct regions within medical images, such as tissue boundaries and vascular structures, without extensive reliance on domain-specific knowledge. The algorithm was evaluated using a standard medical image library, showing superior performance metrics compared to existing methods, thereby demonstrating its potential in enhancing automated medical diagnostics through deep learning