Table of Contents
This project was part of a competition for by the SCQM Foundation (Swiss Clinical Quality Management in Rheumatic Diseases) and won for best performance. The task was to classify X-Ray images into one of 11 distinct location sites ( classes) as the labels for these images had been lost.
This machine learning solution has prevented having to manually and individually label more than 10'000 X-ray images which has saved time and costs to the organization. Furthermore, the algorithm can be deployed continuously to perform annotation of new images, freeing up time for practitioners.
🌟 Highlights:
- Codebase follows PEP8 coding style guidelines, ensuring clean, readable, and maintainable code.
- Current state-of-the-art model in computer vision algorithms for smaller datasets.
- Ensemble combines two architectures, leveraging their complementary strengths and making the classification more robust.
File structure:
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Make sure to install the dependencies:
python -m pip install -r requirements.txt...
Found a bug? Want more features? Find something missing in the documentation? Let me know! Please don't hesitate to file an issue and make a recommendation.
- The original dataset was received from the SCQM Foundation (Swiss Clinical Quality Management in Rheumatic Diseases).
- For compliance to healthcare-specific privacy regulation the dataset will not be disclosed publicly.
- All published models and artifacts have been evaluated to prevent inadvertent disclosure of confidential information.
Classification of X-Ray images - an ensemble CNN approach
The MIT License (MIT)
Copyright (c) 2023 Thierry Grimm
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