ModelsGenesis
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question about pre-processing and network architecture
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
Hi @yaosh09
I have released the reference code for the target segmentation task, with data preprocessing and visualization. Processing data is not very complicated—the final input is 64x64x32 and the intensity value being [0, 1]. I also provided processed data in Google Drive for your reference.
https://github.com/MrGiovanni/ModelsGenesis/blob/master/keras/downstream_tasks/lung%20nodule%20segmentation.ipynb
For model architecture visualization, you could use Keras code
model.summary()
Thanks, Zongwei
Hi @MrGiovanni !
My task is for binary classification. Thank you very much for your answer.