This is probably most similar to #42 and #312, but reading through those did not provide me any clarity. I am just trying to train a trivial whole-image classification network to get a feel for the NiftyNet framework, and running into significant difficulty understanding the process and results.
I have sets of T1-weighted and T2-weighted MR images and I'm trying to train a network to discriminate the two. My label images are 1x1x1 int32 NIfTI volumes with label_0.nii having voxel value 0 and label_1.nii having voxel value 1.
After python net_classify.py train -c config.ini and python net_classify.py inference -c config.ini, I have csv files in the model/output folder that look like this:
csv_67__niftynet_out.csv
,0
0,0.0
1,0.0
2,0.0
I guess I have a couple of questions.
I am new to NiftyNet -- is there is a misconfiguration for what I'm trying to achieve?
If the configuration is correct, what is the correct interpretation of the output above? I am expecting a prediction of '1' or '0'.
This is probably most similar to #42 and #312, but reading through those did not provide me any clarity. I am just trying to train a trivial whole-image classification network to get a feel for the NiftyNet framework, and running into significant difficulty understanding the process and results.
I have sets of T1-weighted and T2-weighted MR images and I'm trying to train a network to discriminate the two. My label images are 1x1x1 int32 NIfTI volumes with
label_0.nii
having voxel value 0 andlabel_1.nii
having voxel value 1.config.ini
subjects.csv
labels.csv
split.csv
After
python net_classify.py train -c config.ini
andpython net_classify.py inference -c config.ini
, I have csv files in the model/output folder that look like this:csv_67__niftynet_out.csv
I guess I have a couple of questions.