Comments (4)
GuobinZhangTJU
commented on July 17, 2024
1
ok, many thanks. thank you ~
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wentaozhu
commented on July 17, 2024
You may use the pre-processing on LIDC-IDRI from https://github.com/wentaozhu/DeepLung
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GuobinZhangTJU
commented on July 17, 2024
in your nodule detection and classification, the input data is cropped CT image data, i don't know the input of tumor segmentation network is also cropped CT image data or only turmor area (extracted according to annoations)? many thanks.
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wentaozhu
commented on July 17, 2024
The deeplung crop is to crop the lung region so you can still use it. Please cite our papers if they are helpful. Thank you!
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Related Issues (20)
- How did you pre-process the Data? I mean, how did you convert the dicom images to numpy arrays.... HOT 2
- Is there any code available for visualizations? HOT 2
- Is the trained-model saved somewhere, in drive or cloud?? Its taking too long to train HOT 2
- Model is not training for 3rd organ(Mandible) HOT 8
- Is code available for Evaluation? HOT 2
- What is the logic for training with different optimizers? HOT 1
- About Pytorch version HOT 11
- Question regarding fdl_loss_wmask implementation. HOT 1
- 关于preprocess的遇到一点问题 HOT 11
- difference between baselineSERes18Conc and anatomy net HOT 4
- about model file HOT 1
- How to remove CT scanner artifacts from the images HOT 2
- Error about TypeError: slice indices must be integers or None or have an __index__ method HOT 1
- About the loss used in this paper HOT 1
- About the loss HOT 3
- About the finetuning
- Training
- it seems the baseline outperforms the anatomyNet? HOT 4
- hybrid loss of AnatomyNet HOT 1
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