This repository provides a SwaV (Swapping Assignments between Views) implementation for self-supervised pretraining on medical images, using PyTorch Lightning and MONAI. It focuses on ablation studies and feature extraction for thymus grading.

• SwaV Pretraining: Self-supervised learning using Swapping Assignments between Views.
• ResNet Backbone: Uses MONAI's ResNet50 adaptation for 3D/2D medical imaging.
• Lighter Integration: Configurable training pipeline using Lighter.
• Technical Ablations: Includes notebooks and scripts for technical ablation studies.
• YAML Configuration: Centralized configuration for training and feature extraction.

. ├── train.yaml # Main SwaV training configuration ├── get_features.yaml # Feature extraction configuration ├── models/ │ └── swav.py # SwaV model implementation ├── losses/ │ └── swav_loss.py # SwaV loss function ├── transforms/ # Data augmentations ├── datasets/ # Data loading wrappers ├── analysis/ # Notebooks for technical ablations ├── README.md └── pyproject.toml # Project dependencies 

This project uses uv for dependency management.

1. Clone the repository:

   git clone <repo-url> cd <repo-root>

2. Install dependencies:

   pip install . # OR using uv uv sync

Run the training using the root configuration file:

lighter fit train.yaml

You can override parameters via the CLI:

lighter fit train.yaml trainer::max_epochs=100 trainer::devices=1

Extract features from a trained model using the feature extraction config:

lighter predict get_features.yaml

• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (SwaV)
• MONAI (Medical Open Network for AI)
• PyTorch Lightning
• Lighter

This project is for research purposes.