AFACNet: Adaptive Frequency Learning Network With Anti-aliasing Complex Convolutions For Colon Diseases Subtypes (JBHI)

Requirements

Our code is based on python and pytorch1.8.

Training the Model

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.

K. Wang et al., "Adaptive Frequency Learning Network With Anti-Aliasing Complex Convolutions for Colon Diseases Subtypes," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 10, pp. 4816-4827, Oct. 2023, doi: 10.1109/JBHI.2023.3300288.