Comments (1)
haydengunraj
commented on July 25, 2024
The discrepancy is due to two main factors. First, since we are doing per-slice detection (2D), we require slice-level ground-truth labels. Slice-level labels are provided in the Cell dataset, but only for a subset of patients. Second, many cases in the Cell dataset are segmented (i.e., the body surrounding the lungs is masked out), and so we had to exclude these cases as well. This leaves us with ~1800 patients, whereas the original dataset had ~3000.
from covidnet-ct.
Related Issues (20)
- example code HOT 2
- COVID-Net CT-2 HOT 4
- Mark images HOT 2
- Preprocessing DICOM to PNG HOT 3
- Hyperparameter-tuning HOT 1
- Window set for prediction HOT 5
- Are the results per image only? HOT 3
- About model COVID-Net CT-2 L (2A) HOT 1
- Model implementation HOT 1
- How to train the model from scratch without using pretrained model ? HOT 1
- details workflow? HOT 1
- checkpoint file causing errors. HOT 1
- What's the batch size and epoch considered for training ? HOT 3
- TENSORFLOW 2.3.0 COMPATIBILITY HOT 1
- problem with inference of covid-net-ct-a HOT 9
- CT labels HOT 1
- Index error HOT 1
- Test vs Inference results for COVID sensitivity HOT 4
- missing images HOT 14
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from covidnet-ct.