FFCNet

FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

Our paper has been accepted by MICCAI 2022.

Prerequisites

Our code is based on python3.6 and pytorch1.1.

Training the networks

python train_test.py

train_dataset-root: Folder to which you downloaded and extracted the training data

val_datapath-root: Folder to which you downloaded and extracted the val data

record_path: The path where the training results are stored

model_path = The path where the model is stored

best_path = The path where the model with the best result on the validation set is stored

First go into the train_test and adapt all the paths to match your file system and the download locations of training and test sets.

Then python train_test.py to train your dataset.

Citation

If you find the code useful for your research, please cite our paper.

Wang, Kai-Ni, et al. "Ffcnet: Fourier transform-based frequency learning and complex convolutional network for colon disease classification." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022.