Applying Conditional Generative Adversarial Networks for Imaging Diagnosis
This work addresses automated medical diagnostics for healthcare applications, but it is incremental as it builds on existing C-GAN and SHGN methods with hybrid loss functions.
This study tackled the problem of overfitting in medical image segmentation by applying Conditional Generative Adversarial Networks with Stacked Hourglass Networks and data augmentation, achieving superior performance metrics compared to existing methods on intravascular ultrasound imaging.
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