These are the weights for the best models reported in the papers.

1,3: https://www.medrxiv.org/content/medrxiv/early/2020/10/14/2020.10.11.20211052.1.full.pdf

2,4: https://www.medrxiv.org/content/medrxiv/early/2020/10/27/2020.10.23.20218461.full.pdf

Download the pretrained weights, zipped file (~590Mb):

https://drive.google.com/file/d/177dY9jSSCsk-de2pAH9TZnO6TaC56VfX/view?usp=sharing

1. Segmentation weights for two positive classes: segmentation_model_two_classes.pth, Ground Glass Opacity and Consolidation segmentation predictions.

2. Segmentation weights for one class (merged masks): segmentation_model_merged_masks.pth, lesion predictions.

3. Lightweight segmentation model (ResNet34+FPN backbone, truncated last block): lightweight_segmentation_model_resnet34_t1.pth, lesion prediction.

4. Lightweight segmentation model (ResNet18+FPN backbone, truncated last block): lightweight_segmentation_model_resnet18_t1.pth, lesion prediction.

5. COVID-CT-Mask-Net: classification_model_two_classes.pth. The best classification model derived from the segmentation model with two classes.
   I get 95.64% overall accuracy on the test data, 93.88% COVID sensitivity on the test split of CNCB CT scans dataset (21192 images).

6. COVID-CT-Mask-Net: classification_model_merged_masks.pth. The best classification model derived from the segmentation model with the merged masks.
   I get 96.33% overall accuracy on the test data, 92.68% COVID sensitivity on the test split of CNCB CT scans dataset (21192 images).

7. Lightweight COVID-CT-Mask-Net (ResNet34+FPN backbone): lightweight_classifier_resnet34_t1.pth with 11.74M weights. I get 92.89% overall accuracy on the test data,
   91.76% COVID sensitivity on the test split of CNCB CT scans dataset (21192 images).

8. Lightweight COVID-CT-Mask-Net (ResNet18+FPN backbone): lightweight_classifier_resnet18_t1.pth with 6.35M weights. I get 93.95% overall accuracy on the test data,
   91.35% COVID sensitivity on the test split of CNCB CT scans dataset (21192 images).