This repository contains the official manuscript and a Jupyter Notebook for my master's thesis, focused on distinguishing Enchondroma and Atypical Cartilaginous Tumor (ACT) using conventional radiographs.
- manuscript.pdf: The complete written thesis, including research background, methodology, results, and conclusions.
- Uncertainty-in-Bone-Tumor-Classification.ipynb: A comprehensive Jupyter Notebook demonstrating data processing, model development, inference, and explainability techniques.
The goal of this project is to leverage a Deep Learning model to classify two types of bone tumor lesions that appear similar on X-ray images. The study uses Explainable AI (XAI) techniques, specifically SHapley Additive exPlanations (SHAP), to improve model interpretability and identify the most critical features contributing to predictions.
- Read through the manuscript.pdf to get a comprehensive understanding of the research.
- Explore the notebook for detailed code, analysis, and visualizations.
Install dependencies by running:
pip install -r requirements.txt
For questions or further discussions, feel free to reach out at: [[email protected]].
This project is licensed under the MIT License.
This thesis was completed with the support of the Lab for AI in Medicine at the Technical University of Munich and the Department of Orthopaedics and Sports Orthopaedics at the Klinikum rechts der Isar.
The dataset used in this project is private and belongs to the hospital. It cannot be shared publicly due to confidentiality agreements and patient privacy regulations. Any results or code shared in this repository are based on this dataset but do not contain any identifiable or sensitive patient information.