Comments (4)
wentaozhu
commented on July 17, 2024
1 Finetune is always a better option.
2 Yes. PDDCA is too small. You can directly download my preprocessed data.
3 Yes. Basically, one day training should be enough.
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debbiely77
commented on July 17, 2024
Yes. I'm trying your entire dataset and it looks well since the loss decreases as it supposed to be. By the way, there is a lossweight for each organ in your code. How do you set them, manually or in any other way?
Sorry for so much questions.
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wentaozhu
commented on July 17, 2024
Please read the paper for the details.
AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy https://www.biorxiv.org/content/biorxiv/early/2018/08/16/392969.full.pdf
Most read award in Medical Physics. Thank you!
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Faithyf
commented on July 17, 2024
@debbiely77 Can you share the PDDCA dataset ?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
- 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
- about lung nodule/tumor segmentation HOT 4
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