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https://github.com/paras42/Hello_World_Deep_Learning
Hello World Introduction to Deep Learning for Medical Image Classification
https://github.com/paras42/Hello_World_Deep_Learning
Last synced: 28 days ago
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Hello World Introduction to Deep Learning for Medical Image Classification
- Host: GitHub
- URL: https://github.com/paras42/Hello_World_Deep_Learning
- Owner: paras42
- Created: 2018-01-03T21:19:06.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-04-25T13:56:47.000Z (8 months ago)
- Last Synced: 2024-08-03T06:01:15.144Z (4 months ago)
- Language: Jupyter Notebook
- Size: 13.3 MB
- Stars: 32
- Watchers: 1
- Forks: 22
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome_medical - Hello_World_Deep_Learning
README
# Hello_World_Deep_Learning
Author: Paras Lakhani, [email protected]
More details and a step-by-step guide for the tutorial can be found in the Journal of Digital Imaging Publication (DOI: 10.1007/s10278-018-0079-6; https://pubmed.ncbi.nlm.nih.gov/29725961/), which is the official journal of the Society of Imaging Informatics in Medicine (SIIM).
This is a high-level introduction into practical machine learning for purposes of medical image classification.
In this tutorial, we use the Tensorflow framework and the Keras library, which a high-level application programming interface that simplifies working with Tensorflow.
We hope that this tutorial will spark interest and provide a basic starting point for those interested in machine learning in regard to medical imaging.
A Jupyter ipython notebook is provided called "HelloWorldDeepLearning.ipynb"
We provide 75 images, 38 are chest X-rays, and 37 are abdominal X-rays. These de-identified PNGs obtained from openI, https://openi.nlm.nih.gov/, a searchable online repository of medical images from published PubMed Central articles
The goal of this tutorial is to build a deep learning classifier to accurately differentiate between the two.
You'll need a computer with the following installed:
1) Tensorflow (https://www.tensorflow.org)
2) Keras library (https://keras.io)
3) Jupyter (http://jupyter.org)
4) Download the x-rays provided in .zip file
To make things easier, there is a convenient SIIM docker that has Tensorflow / Keras / Jupyterlab already installed, located here: https://github.com/ImagingInformatics/machine-learning/tree/master/docker-keras-tensorflow-python3-jupyter
After your environment is set up, open the ipython notebook, and run the code!