Pathology AI is the application of various generative AI in healthcare especially in pathology. Pathology is a branch of medical science that is focused on the study and diagnosis of disease. Pathology AI will be a combination of diffusion models,vision transformers and LLM. This will revolutionize the world of healthcare.

Give me any knee x-ray image , I will tell you where there is arthritis. You can generate Knee x-ray of 5 different level of arthritis using this model. Train it in your own data and share it as medical dataset without fearing for privacy.

1. Vairational autoencoder (VAE) is trained to convert input images (112x112) into a latent space of 3x28x28.
2. Diffusion model (DDPM) is tranined to generate x-ray images of different level of Knee Arthritis. Actually DDPM is trained in latent space and the autoencoder is used for encoding and decoding to grayscale image of size 112x112.
3. The same diffusion model is used to do image to image translation. A healthy image is generated from a given diseased image.
4. Comparsion between original diseased image and the generated healthy image as heat map.

Make a conda environment for better experience.

1. Install pytorch for GPU . Follow the offical website for installing https://pytorch.org/get-started/locally
2. Install opencv. Follow the offical website for installing https://docs.opencv.org/3.4/d2/de6/tutorial_py_setup_in_ubuntu.html
3. Install monai , monai-generative , gradio and tqdm

pip3 install monai==1.2.0  ## Errors comes due to different versio. Check the version
pip3 install monai-generative
pip3 install gradio
pip3 install tqdm

Additionally, please ensure you have a compatible version of MONAI installed. You can find more information on installing MONAI at MONAI Installation Guide.

I used Kaggle dataset - Knee Osteoarthritis Dataset with Severity Grading https://www.kaggle.com/datasets/shashwatwork/knee-osteoarthritis-dataset-with-severity?resource=download ** GPU is needed for training.

1. First download the dataset
2. Open Latent_space_using_autoencoder.ipynb . Train the autoencoder first. Then save the model
3. Open generate_classifier_free_guaidance_bone_condition.ipynb Train the diffusion model , embedding models and save the models.
4. Open anomaly_detection_knee.ipynb In this notebook anomaly detection is implemented. All the trained models are used here to generate healthy x-ray image from a diseased one.

** Change the paths of the trained models according to directory where you saved the models , when they are used in anomaly_detection_knee.ipynb and ui_main_anomaly.py.

If you don't want to train or just want to test. Here is drive link . Download them https://drive.google.com/drive/folders/165e5Bw253Y_8FTmKfgmP9x1WVeWVjmDz?usp=sharing

Gradio based UI is presented here below you can run it in your localhost.

You will need GPU for running this. Load the trained models properly in ui_main_anomaly.py

python3 ui_main_anomaly.py
or
gradio ui_main_anomaly.py

Go to the url printed in the terminal Enjoy your generative model !!.

• Input size - 112x112
• Latent space size - 3x28x28

From a given diseased x-ray image a healthy image is generated. We can think in other way like the model generate an x-ray image as if the same person doesn't have the disease.

Contributions to this project are welcome. Feel free to submit bug reports, feature requests, or even pull requests to improve the functionality and usability of this tool.

This project is licensed under the MIT License - see the LICENSE file for details. Make sure to review and comply with the licensing terms when using or modifying this project for your own purposes.

If you have any questions or need further assistance with the project, please don't hesitate to reach out to the project maintainers.

Thank you for using "Generative AI for Anomaly Detection for Bone Diseases"!