This is the supplementary material for the oral-presentation paper of MICCAIW-MLMI 2019:
"Distanced LSTM: Time-Distanced Gates in LSTM to adapt Longitudinal Lung Cancer Detection".
and Journal Version:
"Time-Distanced Gates in Long Short Term Memory Networks", Medical Image Analysis, 2020 (IF=11.15).
Please cite our paper if you find our work is helpful to you.
In the python code script we have 4 functions:
- get_csv_v1:
This function generate the meta information for tumor-CIFAR-v1 and save it in a csv file. The meta information includes: image name, image time point, nodule position, ground truth (cancer or non-cancer) and nodule size.
- get_csv_v2:
This function is for tumor-CIFAR-v2, serve the same function as get_csv_v1.
- get_nodule_img:
Generating the image according to meta information from csv file.
- add_nodule:
This function is called by get_nodule_img, which transfer the nodule information to image and add noise.
There is an example showing how to use the data in demo_submit.ipynb.
python new_main.py
Note there is a config file named cifar10.yaml
The file TumorCIFAR_materials.pdf describes why and how we create the Tumor-CIFAR. Please email Riqiang Gao (riqiang.gao@vanderbilt.edu) if you have further concerns.