Integrating GANs, Contrastive Learning, and Transformers for Robust Medical Image Analysis

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Convolutional Neural Networks (CNNs) show success in general computer vision but face challenges in medical imaging.
• Limitations include limited labeled data, class imbalance, and inadequate feature representation for subtle medical patterns.

Purpose of the Study:

• To propose CTNGAN, a unified framework integrating generative modeling, Generative Adversarial Networks (GANs), contrastive learning, and Transformers.
• To enhance the robustness and accuracy of medical image analysis by addressing data scarcity, imbalance, and feature representation issues.

Main Methods:

• CTNGAN utilizes GANs to mitigate data scarcity and imbalance.
• Contrastive learning is employed to strengthen feature robustness against domain shifts.
• Transformer architectures are integrated to capture long-range spatial patterns in medical images.

Main Results:

• CTNGAN achieved up to 98.5% accuracy and an F1-score of 0.968 on benchmark medical imaging datasets.
• The framework demonstrated superior generalizability compared to existing methods.
• Joint optimization of data generation, feature discrimination, and contextual modeling was achieved.

Conclusions:

• CTNGAN offers a novel paradigm for accurate and reliable medical image diagnosis.
• The tripartite integration overcomes limitations of conventional CNNs in medical image analysis.
• The framework establishes a new standard for robust and generalizable medical AI solutions.