GAN_MRI_StyleTransfer

Problem Statement

Misdiagnosis in medical imaging, especially in MRI interpretation, is a significant concern due to variations in radiologists' observations. Conflicting reports and challenges in recommending treatments can arise. This project aims to address this by enhancing diagnostic accuracy through Style Transfer using Generative Adversarial Networks (GANs) on MRI images. The objective is to generate artificial MRI images with varying contrast levels, aiding in more nuanced and accurate diagnoses.

Dataset

The dataset, consists of unpaired T1 and T2 MRI images. Notably, the images are unrelated, creating an unpaired dataset challenge.

Technologies Used

The project is implemented in Python, primarily utilizing the TensorFlow library for deep learning tasks. Additional libraries include NumPy for numerical operations, Matplotlib for visualization, OpenCV for image processing, and pathlib for handling file paths.

Project Steps

1. Data Understanding:

   • Load and preprocess the dataset, ensuring its suitability for deep learning tasks.

2. Image Processing:

   • Apply various image processing techniques, including normalization, resizing, and reshaping, to enhance data quality.

3. Model Building and Training:

   • Construct Generators and Discriminators using a modified U-Net architecture, inspired by CycleGAN.
   • Define and implement loss functions and training steps for effective model training.

4. Style Transfer:

   • Implement Style Transfer functionality using GANs to translate the style of one MRI image to another.

5. Evaluation and Validation:

   • Assess the model's performance using appropriate metrics.
   • Validate the generated images against ground truth data.

6. Application in Medical Imaging:

   • Explore the potential applications of Style Transfer in medical imaging beyond MRI, such as CT scans and X-rays.

Conclusion

GAN_MRI_StyleTransfer represents a pioneering effort in leveraging advanced deep learning techniques for medical image enhancement. By addressing the challenges of misdiagnosis, this project has implications for improving patient care and treatment outcomes. The generated artificial images provide a richer context for radiologists, potentially reducing the risk of conflicting reports.

Beyond its immediate application in medical imaging, the principles explored in this project have broader implications in image translation and enhancement across various domains, contributing to the advancement of deep learning applications.