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Description
The goal of this issue is to track the training and performance of a single model(s) (instead of fine-tuning).
Result comparisons and observations: report
Steps involved:
- Concatenate all the subjects from Systematic review of binary ground truth quality #25, Training and inference discussion for active learning round 1 #35, Training and inference discussion for active learning round 2 #38 and Training and inference discussion for active learning round 3 #40 into one dataset (will be called
data_superset
from now on) - Take the default 3d_fullres nnUNetv2 generated plan using
nnunetv2_preprocess
(here) and train a model. The plan configured by nnUNetv2 is attached below:
nnUNetPlans.json
To reproduce the experiment, run the following command:
nnUNetv2_preprocess -d <DATASET_ID> -c 3d_fullres
After the training was completed inference was run on the held-out_test
set (#33) using this script
Note: This process is repeated for all the following comments also:
Attaching the QC for qualitative results:
held-out_test_common_bids_qc.zip
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