DCGAN - Deep Convolutional Generative Adversarial Networks

Overview

This project implements Deep Convolutional Generative Adversarial Networks (DCGAN) for Medical Image Synthesis. DCGANs are a class of neural networks that use deep convolutional neural networks for both the generator and discriminator, making them particularly effective for image generation tasks.

Description

Deep Convolutional Generative Adversarial Networks (DCGANs) combine the power of Generative Adversarial Networks (GANs) with deep convolutional neural networks to generate realistic synthetic images. This implementation focuses on medical image synthesis, which has important applications in:

• Data augmentation for medical imaging datasets
• Privacy-preserving medical research
• Training robust medical image analysis models
• Generating synthetic samples for rare medical conditions

Architecture

The DCGAN architecture consists of two main components:

Generator

• Takes random noise as input
• Uses transposed convolutions to upsample features
• Generates synthetic medical images

Discriminator

• Takes images as input (both real and generated)
• Uses convolutional layers to classify images as real or fake
• Provides feedback to improve the generator

Features

• Implementation of DCGAN architecture for medical imaging
• Training pipeline with configurable hyperparameters
• Image generation and visualization utilities
• Support for various medical image datasets

Requirements

torch
torchvision
numpy
matplotlib
Pillow
jupyter

Usage

Open and run the Jupyter notebook:

jupyter notebook DCGAN.ipynb

The notebook contains:

1. Data loading and preprocessing
2. Model architecture definition
3. Training loop implementation
4. Image generation and visualization
5. Results and analysis

Model Training

The model is trained using:

• Binary Cross Entropy loss
• Adam optimizer
• Learning rate scheduling
• Batch normalization
• LeakyReLU activations

Results

The trained model generates synthetic medical images that closely resemble real medical imaging data, while maintaining patient privacy and data diversity.

References

• Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.
• Goodfellow, I., et al. (2014). Generative Adversarial Networks.

License

This project is available for educational and research purposes.

Author

Bhavana Meravanige Veerappa

Acknowledgments

This implementation is based on the original DCGAN paper and adapted for medical image synthesis applications.