MFADNet

Multiple Field-Of-View Based Attention Driven Network For Weakly-Supervised Common Bile Duct Stone Detection

This is the website reserved for MFADNet released code.

The paper is published in IEEE Journal of Translational Engineering in Health and Medicine, 2023

https://ieeexplore.ieee.org/document/10153581

This code is modified from https://github.com/lim-anggun/FgSegNet_v2

Lim, L.A. & Keles, H.Y. Pattern Anal Applic (2019). https://doi.org/10.1007/s10044-019-00845-9

Usage

Put the dataset in ./MFADNet/new_datasets and the label in ./MFADNet/new_training_label

The file format can be refer to exmple files in the folder

Instructions for Code:

Train the model

cd MFADNet
train.py

Save the output

cd MFADNet
train.py

Requirements

The most important packages:

keras                   2.9.0
tensorflow-gpu          2.5.0

else

absl-py                 0.15.0
asttokens               2.2.1
astunparse              1.6.3
backcall                0.2.0
cachetools              5.2.0
certifi                 2022.6.15
charset-normalizer      2.0.12
colorama                0.4.6
cycler                  0.11.0
decorator               5.1.1
executing               1.2.0
flatbuffers             1.12
fonttools               4.33.3
gast                    0.4.0
google-auth             2.8.0
google-auth-oauthlib    0.4.6
google-pasta            0.2.0
grpcio                  1.34.1
h5py                    3.1.0
importlib-metadata      4.12.0
ipython                 8.10.0
jedi                    0.18.2
joblib                  1.1.0
keras-contrib           2.0.8
keras-nightly           2.5.0.dev2021032900
Keras-Preprocessing     1.1.2
keras-vggface           0.6
kiwisolver              1.4.3
Markdown                3.3.7
matplotlib              3.5.2
matplotlib-inline       0.1.6
mediapipe               0.8.10.1
numpy                   1.19.5
oauthlib                3.2.0
opencv-contrib-python   4.6.0.66
opt-einsum              3.3.0
packaging               21.3
parso                   0.8.3
pickleshare             0.7.5
Pillow                  9.1.1
pip                     22.1.2
prompt-toolkit          3.0.36
protobuf                3.19.4
pure-eval               0.2.2
pyasn1                  0.4.8
pyasn1-modules          0.2.8
Pygments                2.14.0
pyparsing               3.0.9
python-dateutil         2.8.2
PyYAML                  6.0
requests                2.28.0
requests-oauthlib       1.3.1
rsa                     4.8
scikit-learn            1.1.1
scipy                   1.8.1
setuptools              63.4.1
six                     1.16.0
stack-data              0.6.2
tensorboard             2.9.1
tensorboard-data-server 0.6.1
tensorboard-plugin-wit  1.8.1
tensorflow-estimator    2.5.0
tensorflow-gpu          2.5.0
termcolor               1.1.0
threadpoolctl           3.1.0
traitlets               5.9.0
typing-extensions       3.7.4.3
urllib3                 1.26.9
wcwidth                 0.2.6
Werkzeug                2.1.2
wheel                   0.37.1
wincertstore            0.2
wrapt                   1.14.1

Reference

Please cite the following paper when you apply the code.

Y.-H. Chang, M.-Y. Lin, M.-T. Hsieh, M.-C. Ou, C.-R. Huang and B.-S. Sheu, "Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection," IEEE Journal of Translational Engineering in Health and Medicine, vol. 11, pp. 394-404, 2023, doi: 10.1109/JTEHM.2023.3286423.