# Applying Conditional Generative Adversarial Networks for Imaging Diagnosis

arXiv: https://arxiv.org/abs/2408.02074 · HTML: https://mlsanity.com/paper/2408.02074/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification](https://mlsanity.com/paper/2404.14606/) (2024)
- [Research on Image Recognition Technology Based on Multimodal Deep Learning](https://mlsanity.com/paper/2405.03091/) (2024)
- [Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data](https://mlsanity.com/paper/2406.00016/) (2024)
- [DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era](https://mlsanity.com/paper/2404.12278/) (2024)
- [Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis](https://mlsanity.com/paper/2405.17459/) (2024)
- [Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis](https://mlsanity.com/paper/2405.20603/) (2024)
- [Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning](https://mlsanity.com/paper/2406.08838/) (2024)

## Built on by (most cited first, 9 of 9)

- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment](https://mlsanity.com/paper/2409.18544/) (2024)
- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis](https://mlsanity.com/paper/2410.04283/) (2024)
- [A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining](https://mlsanity.com/paper/2410.15026/) (2024)
- [Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study](https://mlsanity.com/paper/2410.20792/) (2024)
- [Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation](https://mlsanity.com/paper/2409.13868/) (2024)
- [Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature](https://mlsanity.com/paper/2409.14000/) (2024)
- [AdaMixup: A Dynamic Defense Framework for Membership Inference Attack Mitigation](https://mlsanity.com/paper/2501.02182/) (2025)

## Abstract (arXiv)

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