Feature Extraction
Transformers
Safetensors
medical
radiology
vision-language
chest-ct
3d-medical-imaging
glint
custom_code
Instructions to use Deepnoid/GLINT-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deepnoid/GLINT-CT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Deepnoid/GLINT-CT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Deepnoid/GLINT-CT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download data/README.md from Deepnoid/GLINT-CT: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/Deepnoid/GLINT-CT/resolve/main/data/README.md
- Command line
-
hf download hf://Deepnoid/GLINT-CT/data/README.md
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curl -L -o README.md https://huggingface.co/Deepnoid/GLINT-CT/resolve/main/data/README.md
1.21 kB
| # Data splits (chest CT) | |
| - Split ID lists; download each dataset from its source under its own terms. | |
| - `val` is for model selection; `test` is official unless noted. | |
| | Dataset | Files | ID format | Train / Val / Test | Notes | | |
| |---|---|---|---|---| | |
| | LIDC-IDRI | `splits/lidc_idri/` | `PatientID/StudyUID/SeriesUID` | 705 / 148 / 157 | Patient-disjoint | | |
| | RSNA-PE | `splits/rsna_pe/` | `StudyUID/SeriesUID` | 4,983 / 1,062 / 1,060 | Study-level (Ke et al., MIDL 2024) | | |
| | MSD-Lung | `splits/msd_lung/` | MSD case id | 44 / 6 / 13 | Holdout of 63 cases | | |
| | NSCLC-Radiomics | `splits/nsclc_radiomics/` | `LUNG1-XXX` | 294 / 42 / 84 | LUNG1-035/128 excluded (corrupt) | | |
| | CT-RATE | `splits/ct_rate/` | volume name | 43,400 / 2,994 / 3,002 | `test` = official validation (1,304 patients); `val` = 1,304 patients from the official train | | |
| CT-RATE notes (the lists already exclude CT-RATE's non-chest volumes and 3 unreadable ones): | |
| - **Pretraining:** also excludes `pretrain_exclusions.txt` (ReXGroundingCT overlap) and volumes with an empty report decomposition → 43,337 / 2,990 / 3,002. | |
| - **Fine-tuning:** also excludes 11 unusable volumes (`ft/chest_ct/*/dataset/ct_rate.py`) → 43,390 / 2,993 / 3,002. | |