MrGiovanni / MrGiovanni/ModelsGenesis
question about pre-processing and network architecture
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Description
This is a wonderful work and has great impact on 3D medical imaging deep learning.
I am trying to fine-tune Models Genesis to classify HCC pathological biomark (negative OR positive) based on liver CT data.
I'm fairly new in this field and there is a question about data pre-processing. I have abdomen CT image datas (DICOM files) and HCC ROI segmentation result (3D nii file, which value is 0 or 1 to identify the ROI region ), i don't konw how to process them to put into the model？ Merge them into one npy file?
Could you please list the network architecture of 3D Models Genesis? I plan to employ the encoder as a fixed feature extractor.
Thank you for your help,
Best Regards.
Shanhu
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Research direction
No files, tests, or entry points are named. Start by reviewing the repository's preprocessing guidance and the included Keras and PyTorch weights, then document how DICOM volumes and 3D NIfTI ROI masks should be prepared for fine-tuning and describe the 3D Models Genesis network architecture and encoder boundaries.
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Assessment
- Tech stack
- keras, numpy, pytorch
- Domain
- computer-vision, data, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100