Explore Medical Image Segmentation with Deep Learning
Artificial intelligence and deep learning are revolutionizing diagnostics in healthcare and medical science. My project is dedicated to the accurate identification of polyps in colonoscopy images, offering an essential tool for medical professionals.
The use of the CVC-Clinic database, containing frames from colonoscopy videos. The dataset includes polyp frames and corresponding ground truth images in both PNG and TIFF formats.
I aimed to develop a robust polyp segmentation model using deep learning techniques.
- Language: Python
- Deep Learning: PyTorch
- Computer Vision: OpenCV
- Libraries: Scikit-learn, Pandas, NumPy, Albumentations, and more.
- Data Insight: Understand the dataset.
- Evaluation Metrics: Grasp the evaluation criteria.
- Unet Architecture: Explore Unet for medical applications.
- Unet++ Advantage: Discover the benefits of Unet++.
- Environment Setup: Get your project environment ready.
- Data Augmentation: Enhance data for better performance.
- Model Building: Create the Unet++ model with PyTorch.
- Model Training: Train the model (GPU recommended).
- Model Prediction: Understand the modular code structure.
- input: Contains data (PNG and TIFF folders).
- src: The heart of the project with modular code, including ML pipelines, engine, and config.
- output: Stores trained models and predictions.
- lib: Reference materials (original iPython notebook).
- requirements.txt: Lists all dependencies.
- Polyp Segmentation Insights
- IOU Metric Understanding
- Data Augmentation Techniques
- Practical PyTorch Data Augmentation
- Medical Computer Vision Applications
- Building CNN Models
- OpenCV for Computer Vision
- VGG Architecture Familiarity
- Unet and Unet++ Knowledge
- Building VGG Blocks with PyTorch
- Training Unet++ Models
For questions, collaborations, or further information, feel free to contact me:
- Project Reference: Project Pro
- LinkedIn: LinkedIn Profile
- Email: vaasutiwari@gmail.com
# Clone the repository
git clone https://github.com/YourUsername/YourRepository.git
# Navigate to the project directory
cd Medical-Image-Segmentation-Deep-Learning-Project
# Install dependencies
pip install -r requirements.txt