Code for 《Multi-Task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis》 Project.
| Author | LIU Lihao |
|---|---|
| lhliu1994@gmail.com |
This is the pytorch implementation for 《MTMR-Net: Multi-Task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis》
We can output a more robust benign-malignant classification result with persuasive semantic feature scores compared to other CAD techniques which can only output classification results, as shown in the figures.
pytorch: http://pytorch.org/
tensorboardX: https://github.com/lanpa/tensorboard-pytorch
Download and unzip this project: MTMR-net-master.zip.
Download and unzip preprocessed data into "./data/" folder：
https://drive.google.com/open?id=1xFRQBzuQLv4fO5ecsyKnPc5u2D2N9e76
Download resnet50 model into "./logs/middle_result_logs/imagenet/" folder from pytorch website:
https://download.pytorch.org/models/resnet50-19c8e357.pth
1.Original Dataset:
Original LIDC-IDRI dataset can be found in the official website:
https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
The preprocessing methods can be found in below 2 links:
https://github.com/zhwhong/lidc_nodule_detection
https://github.com/jcausey-astate/NoduleX_code
2.Preprocessed Data:
Find the preprocessed data(2d slices) which can be used directly in the code from Installation section.
- Modify the args.yaml, add the parameters your deep learning model need under the "running_params" item. Details are shown in another project: https://github.com/CaptainWilliam/Deep-Learning-Model-Saving-Helper
- Pass the running_params (a python dict which contains the running parameters) to you own model.
- The first parameter "is_training" is True for training mode, "is_training" is False for test mode.
- Finish you mode(training or test), and run it.
$ cd MTMR-NET-master
$ python main.py😙Thanks my dearest brother yong for this beautiful figure.