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Coronaries-Arteries-Diseases-Weakly supervised Learning based method

this porject is supported by Jordan University of science and Technology , alonge side with this research we explored a paper that introduced Multi-instance Learning approach based on Transformer in our mission we Re-design PPGE method for more efficient training by improvig Convolution with Fast Fourier Transform which called the method Fast Fourier Postional encoding FFPE

Notation: the implementation still under progres as long as we are trying to collect dataset of Coronaries-Arteries-Diseases now tried to test the approach on Data from Kaggle RSNA Screening Mammography Breast Cancer Detection

  • Setup the ENV:

    • Create the environment

       conda create --name TransFFT-MIL python=3.6

    • install the requirements

        pip install -r requirements.txt

  • Run the code :

    • training model
      Note in our experiment we Re-Developed two approaches based Positional Encodings methods FFTPEG and FF_ATPEG that can be changed in TransFFPEG.py file
     python train.py --stage 'train' --gpus 0 --Epochs 200