medical-gan-augmenter

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medical-gan-augmenter

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A web application leveraging PyTorch Generative Adversarial Networks (GANs) to augment limited medical image datasets for robust AI model training. Features automatic GAN architecture selection, cloud-based training via Google Colab, and comprehensive quality evaluation.

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# Medical Image GAN Augmentation Web Application

## Project Overview :

This project aims to develop a user-friendly web application for medical image data augmentation using Generative Adversarial Networks (GANs). The goal is to help researchers and developers overcome data scarcity in the medical domain by generating high-quality synthetic images.

## Features

* **Dataset Upload:** Users can upload their medical image datasets (e.g., DICOM, NIfTI, common image formats).
* **Automatic GAN Architecture Selection:** The application will intelligently select an appropriate 3D GAN architecture based on the uploaded data characteristics.
* **Cloud-Based Training (Google Colab Integration):** Training of the GAN models will leverage the user's own Google Colab account, utilizing their GPU resources.
* **Synthetic Image Generation:** Generate augmented datasets of high-fidelity synthetic medical images.
* **Quality Evaluation & Visualization:** Provide visual and quantitative metrics (e.g., PCA, t-SNE, Fréchet Inception Distance (FID), Inception Score (IS)) to assess the quality and similarity of generated data to the original.
* **Code Download:** Users can download the Python code for the trained GAN architecture for local execution.

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