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