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This issue intends to compare performances between the model trained from scratch on `dcm-zurich-lesions-*` (#1) vs. a model pretrained on `dcm-zurich` for detecting compression sites and using those …
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The idea is to try to leverage T2w images and PSIR/STIR images as we have them both for each subjects. One problem we were facing was that we have a lot of PSIR images and few STIR images: so if we tr…
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The data is stored in `duke/mri/basel/4031_MRI_T2_MRAGE_24patients_CGM` and the segmentations are stored in `duke/mri/basel/4031_Segmentations_T2_MPRAGE_24patients_CGM`
The steps are the following:
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Looking at the code, dataset_conf['data'] corresponds to a label mask. Can't all four categories be trained at once?
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# Description
[SlicerNNUnet](https://github.com/KitwareMedical/SlicerNNUnet) is an extension on [Slicer (v5.7.0)](https://download.slicer.org/) for implement segmentation models trained with [nnUN…
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Hi,
I had a dataset of stroke patients(T1w, fmri, lesion-mask) and tried to run fmriprep to get structural and functional results.
Lesion-mask image was placed and named correctly.
When I chec…
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LST seems like an open-source toolbox, notably used as a baseline in this [paper](https://www.sciencedirect.com/science/article/pii/S2213158220302825) (also mentioned in our Slack channel). Would be g…
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---> cnn1 loading training data
Traceback (most recent call last):
File "train_leave_one_out.py", line 79, in
model = train_cascaded_model(model, train_x_data, train_y_data, options)
Fil…
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Hello everyone,
I am trying to run this great toolbox on HPC. So i used apptainer to build a sif image from the corresponding docker container (jqmcginnis/lst-ai:latest). Afterwards, i run this sif…
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Opening this issue to reference all the data that we have, which is axial T2 and T2* images.