RadBERT-CT
Custom RadBERT sequence-classification model converted from a training checkpoint with:
- backbone initialized from
zzxslp/RadBERT-RoBERTa-4m - Finetuned on CT-RATE reports in the paper "Generalist foundation models from a multimodal dataset for 3D computed tomography"
- Number of labels:
18
Load Model and Tokenizer
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
repo_id = "IAMJB/RadBERT-CT"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
model.eval()
Get Logits + Predicted Positive Class
import torch
texts = [
"No acute cardiopulmonary abnormality.",
"Right lower lobe opacity, suspicious for pneumonia."
]
inputs = tokenizer(
texts,
padding=True,
truncation=True,
max_length=512,
return_tensors="pt",
)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.sigmoid(logits)
pred_mask = probs > 0.5
print("logits:", logits)
print("logits shape:", logits.shape)
print("probs over 0.5:", probs > 0.5) # [batch_size, num_labels]
print("pred label mask:", pred_mask.tolist())
print(
"pred label indices:",
[[i for i, on in enumerate(row) if on] for row in pred_mask.tolist()],
)
Citation
@article{Hamamci2026Generalist,
author = {Hamamci, Ibrahim Ethem and Er, Selim and Wang, Chen and others},
title = {Generalist foundation models from a multimodal dataset for 3D computed tomography},
journal = {Nature Biomedical Engineering},
year = {2026},
month = feb,
day = {12},
doi = {10.1038/s41551-025-01599-y},
url = {https://doi.org/10.1038/s41551-025-01599-y},
publisher = {Springer Nature}
}
Metric available in RadEval
@inproceedings{xu-etal-2025-radeval,
title = "{R}ad{E}val: A framework for radiology text evaluation",
author = "Xu, Justin and
Zhang, Xi and
Abderezaei, Javid and
Bauml, Julie and
Boodoo, Roger and
Haghighi, Fatemeh and
Ganjizadeh, Ali and
Brattain, Eric and
Van Veen, Dave and
Meng, Zaiqiao and
Eyre, David W and
Delbrouck, Jean-Benoit",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.40/",
doi = "10.18653/v1/2025.emnlp-demos.40",
pages = "546--557",
ISBN = "979-8-89176-334-0",
}