CTScanModelingThesis: Bayesian and Deterministic Approaches

Project Description:

• English:

This repository houses the code and resources for my thesis project focused on uncertainty estimation in computed tomography (CT) scan modeling using Bayesian and deterministic neural networks. The primary objective of this research endeavor is to explore and compare the effectiveness of Bayesian methodologies in quantifying uncertainty in CT scan analysis.

• Spanish:

En este repositorio se aloja el código y los recursos para mi proyecto de tesis centrado en la estimación de la incertidumbre en el modelado de tomografías computarizadas (TC) utilizando redes neuronales bayesianas y deterministas. El objetivo principal de esta investigación es explorar y comparar la efectividad de las metodologías bayesianas en la cuantificación de la incertidumbre en el análisis de tomografías computarizadas.

Key Features:

• Bayesian and Deterministic Neural Networks: Implementation of neural network architectures for CT scan modeling.
• Uncertainty Estimation: Exploration of uncertainty estimation techniques to assess model confidence and reliability.
• Comparative Analysis: Comparative analysis of Bayesian and deterministic approaches in CT scan analysis.
• Deep Learning Methodologies: Integration of state-of-the-art deep learning methodologies for improved predictive performance.
• Documentation and Code Samples: Comprehensive documentation and code samples to facilitate understanding and reproducibility of experimental results.

Getting Started:

1. Clone the repository: git clone https://github.com/your_username/ct-scan-modeling-thesis-bayesian-deterministic.git

2. Navigate to the project directory: cd ct-scan-modeling-thesis-bayesian-deterministic

3. Install the required dependencies: pip install -r requirements.txt

Directory Structure:

ct-scan-modeling-thesis-bayesian-deterministic/
│
├── data/              # Directory for storing CT scan data
├── models/            # Directory for storing trained models
├── notebooks/         # Jupyter notebooks for experimentation and analysis
├── scripts/           # Python scripts for model training, evaluation, and visualization
├── utils/             # Utility functions and helper scripts and results
├── LICENSE            # License file
├── README.md          # Project README file
└── requirements.txt   # List of dependencies

License:

This project is licensed under the MIT License.

Contact Information:

For any inquiries or feedback regarding this project, please contact Juan Liscano.