Generative Adversarial Networks for Synthetic Medical Image Augmentation

This research project explores the use of Generative Adversarial Networks (GANs) to synthesize additional medical imaging samples (such as chest X-rays or skin-lesion images) for underrepresented disease classes, addressing the class-imbalance problem common in publicly available diagnostic imaging datasets.

A DCGAN/StyleGAN-based generator is trained on the minority-class images, and the resulting synthetic samples are used to augment the training set for a downstream diagnostic classifier (a CNN), which is then evaluated against a baseline trained only on the original, imbalanced data.

The study reports whether GAN-based augmentation improves classifier sensitivity for the minority class without degrading specificity, uses a radiologist-style visual Turing test alongside quantitative metrics (FID score) to assess synthetic image realism, and discusses ethical considerations around using synthetic data in clinical-adjacent ML pipelines.

Detailed Project Description

Generative Adversarial Networks for Synthetic Medical Image Augmentation applies computer vision to camera frames to detect or recognise what is in view and act on the result automatically.

How It Works
1. The camera captures frames continuously.
2. Frames are pre-processed and fed to the detection/recognition model.
3. Detections are drawn on the output and used to trigger the required action (log, alert, unlock, count).
4. Results and images are stored or streamed to a browser/dashboard.

Technology Roles
- Python
- PyTorch
- GAN
- Medical Imaging
- CNN

Implementation Steps
1. Define requirements and draw the ER / class diagram and screen flow.
2. Set up the environment (Python, PyTorch, Jupyter Notebook) and create the project skeleton.
3. Design the database schema / data model and seed it with sample data.
4. Implement the core modules with validation and role-based access where needed.
5. Build the user interface and connect it to the backend / data layer.
6. Test each module (unit + manual), fix edge cases and secure inputs (SQL injection, XSS, auth).
7. Prepare documentation, screenshots and a demo build.

Key Features
- Real-time detection
- Annotated output
- Event logging
- Adjustable confidence threshold

Expected Output
On completion the student will have a live video/image feed with detected objects highlighted and the corresponding actions logged.

Deliverables
- Complete source code with comments
- Database schema / data model and screen flow
- Project report with screenshots and results
- Demonstration-ready build