This repository contains three different experiments related to brain image analysis. Here's a brief overview of each experiment:
This experiment focuses on training a SWIN model using brain section images obtained from the Allen Institute dataset. The goal is to perform image classification on various brain section classes.
In this experiment, we generate brain section image data by querying specific regions of interest (ROIs) across thousands of full brain acquisitions conducted by the Allen Institute. This data can be used for various brain image analysis tasks.
The Self-supervised_segmentation folder contains the core work of the repository. This section represents the culmination of our Master's thesis and has been published as a scientific paper at MLMI 2023. It introduces a novel architecture for unsupervised segmentation of white matter images using self-supervision techniques.
- Cutting-edge self-supervised segmentation methodology.
- Codebase for replicating the experiments mentioned in the MLMI 2023 paper.
- Detailed documentation for running the experiments and reproducing the results.
To get started with each experiment, please refer to the respective README files in the corresponding experiment folders.
If you find this work useful for your research, please consider citing our paper:
@inproceedings{hawchar2023leveraging,
title={Leveraging Self-attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter Images},
author={Hawchar, Mohamad and Lefebvre, Jo{\"e}l},
booktitle={International Workshop on Machine Learning in Medical Imaging},
pages={247--256},
year={2023},
organization={Springer}
}