ACT volume-report model (clip_3d_ctrate_merlin_v1)
0
3 commits
1 linked in READMEs
updated Aug 24, 2026
Weights for ACT (Auditable CT phenotyping through report-derived radiological observations), the native-resolution 3D vision-language model described in the manuscript under submission. Code: github.com/peterhan91/ACT.
torch.hub), applied to 160 axial slices at 224 x 224.Contrastive volume-report pretraining on CT-RATE (chest CT) and Merlin (abdominal CT): 38,317 patients. The CT-RATE validation split was used only for checkpoint selection. No evaluation scan (CT-RATE test, PMBB, RSNA-2023, INSPECT) was seen in pretraining.
from load_pretrained import load_act # model/load_pretrained.py in the ACT repo
model, preprocess_text = load_act()
The loader downloads clip_3d_ctrate_merlin_v1/best_model.pt from this
repository and instantiates the architecture above. Volumes must be
preprocessed with the ACT pipeline (RAS reorientation, HU clip
[-1000, 1000], aspect-preserving resize and pad to 160 x 224 x 224, uint8).
Released for academic research. Not a medical device; not for clinical decision-making. Performance is characterized in the manuscript on CT-RATE, PMBB, RSNA-2023, and INSPECT. Behaviour outside chest and abdominal CT of adults is uncharacterized. The source datasets' licenses and data-use terms apply to any data you use with the model.
clip_3d_ctrate_merlin_v1/best_model.pt: the released checkpoint, selected
by mean AUROC over the 18 CT-RATE validation labels (0.756 at selection).See the ACT repository CITATION.cff.
A journal reference will be added upon publication.
ACT volume-report model (clip_3d_ctrate_merlin_v1)
0
3 commits
1 linked in READMEs
updated Aug 24, 2026
Weights for ACT (Auditable CT phenotyping through report-derived radiological observations), the native-resolution 3D vision-language model described in the manuscript under submission. Code: github.com/peterhan91/ACT.
torch.hub), applied to 160 axial slices at 224 x 224.Contrastive volume-report pretraining on CT-RATE (chest CT) and Merlin (abdominal CT): 38,317 patients. The CT-RATE validation split was used only for checkpoint selection. No evaluation scan (CT-RATE test, PMBB, RSNA-2023, INSPECT) was seen in pretraining.
from load_pretrained import load_act # model/load_pretrained.py in the ACT repo
model, preprocess_text = load_act()
The loader downloads clip_3d_ctrate_merlin_v1/best_model.pt from this
repository and instantiates the architecture above. Volumes must be
preprocessed with the ACT pipeline (RAS reorientation, HU clip
[-1000, 1000], aspect-preserving resize and pad to 160 x 224 x 224, uint8).
Released for academic research. Not a medical device; not for clinical decision-making. Performance is characterized in the manuscript on CT-RATE, PMBB, RSNA-2023, and INSPECT. Behaviour outside chest and abdominal CT of adults is uncharacterized. The source datasets' licenses and data-use terms apply to any data you use with the model.
clip_3d_ctrate_merlin_v1/best_model.pt: the released checkpoint, selected
by mean AUROC over the 18 CT-RATE validation labels (0.756 at selection).See the ACT repository CITATION.cff.
A journal reference will be added upon publication.