This project focuses on Brain Tumor Segmentation using deep learning techniques. We are leveraging the BraTS 2017 dataset, which contains a variety of MRI scans of brain tumor patients. The primary goal of the project is to develop a model that can accurately segment different tumor regions in brain MRI images, helping in the diagnosis and treatment planning for patients.
Our project aims to preprocess this data, train a segmentation model, and evaluate its performance.
For more details on specific parts of our project, please refer to the following:
- Baseline Model: Description of the baseline model we use.
- Data Acquisition Guide: Steps to acquire the BraTS 2017 dataset.
- Docker Guide: Instructions for setting up and running the project in a Docker environment.
- Evaluation Criteria Definition: Detailed description of the metrics and criteria used to evaluate model performance.
- Model Evaluation: Results and analysis of the performance of the final model.
- Project Structure: A detailed overview of the project's structure and organization.
- References: A list of all references we've used.
- User Guide: Instructions for starting the Gradio interface powered by the machine learning model we developed. This document contains everything you need to start the project (the teacher should read this).