Dense Net MURA Py Torch

Abstract

1. Introduction

A PyTorch implementation of 169 layer DenseNet model on MURA dataset, inspired from the paper arXiv:1712.06957v3 by Pranav Rajpurkar et al. MURA is a large dataset of musculoskeletal radiographs, where each study is manually labeled by radiologists as either normal or abnormal. know more

The model implemented in model.py takes as input 'all' the views for a study of an upper extremity. On each view the model predicts the probability of abnormality. The Model computes the overall probability of abnormality for the study by taking the arithmetic mean of the abnormality probabilites output by the network for each image.

2. Objective

Implementation of DenseNet model on Standford's MURA dataset using PyTorch

This project demonstrates how Python, PyTorch can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/pyaf/DenseNet-MURA-PyTorch.git
cd DenseNet-MURA-PyTorch

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by pyaf and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.