T5FactExtractor — Radiology Fact Extractor

T5FactExtractor is a T5-small sequence-to-sequence model that extracts factual statements from chest X-ray radiology report sentences. Given a sentence, it generates a JSON-like list of short clinical facts that can be embedded, compared, or used in metrics such as CXRFEScore.

It is stage 1 of the Extracting and Encoding framework from Findings of ACL 2024:

1. Fact extraction — this model (pamessina/T5FactExtractor)
2. Fact encoding — pamessina/CXRFE

Paper: Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation

Model details

Architecture	T5ForConditionalGeneration
Base model	t5-small
Task	Sentence → list of radiology facts
Typical use	Preprocess report sentences before encoding with CXRFE
License	Apache 2.0

Output format

The model generates a string containing a JSON array of fact strings, for example:

["small right pleural effusion", "normal heart size"]

Downstream code (including cxrfescore) parses that array, deduplicates facts, and lightly cleans repeated words.

How to use

Standalone (Transformers)

import re
import json
import torch
from transformers import T5ForConditionalGeneration, T5TokenizerFast

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "pamessina/T5FactExtractor"

tokenizer = T5TokenizerFast.from_pretrained(model_id)
model = T5ForConditionalGeneration.from_pretrained(model_id).to(device)
model.eval()

sentence = "There is a small right pleural effusion. The heart size is normal."
# Prefer one sentence at a time (reports are usually sentence-split first).
inputs = tokenizer(sentence, padding="longest", return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs["attention_mask"].to(device)

with torch.no_grad():
    output_ids = model.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        max_new_tokens=input_ids.shape[1] * 4,
        num_beams=1,
    )

raw = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
print("raw:", raw)

# Minimal parse (same idea as cxrfescore.text_utils.parse_facts)
match = re.search(r"\[.*", raw)
if match:
    facts_str = match.group()
    if not facts_str.endswith("]"):
        facts_str += "]"
    facts = json.loads(facts_str)
    print("facts:", facts)

Easiest path: CXRFEScore

For full reports, the package sentence-splits, runs this extractor, aggregates unique facts, and (optionally) embeds them with CXRFE:

pip install cxrfescore

from cxrfescore import CXRFEScore

metric = CXRFEScore(device="cuda")
reports = [
    "There is a small right pleural effusion. The heart size is normal.",
]
facts_per_report = metric.extract_facts(reports)
print(facts_per_report[0])

Demo notebook: CXR-Fact-Encoder / notebooks/cxrfescore_demo.ipynb

Related resources

• Paper hub: https://github.com/PabloMessina/CXR-Fact-Encoder
• Metric package: https://github.com/PabloMessina/CXRFEScore · PyPI
• Companion fact encoder: https://huggingface.co/pamessina/CXRFE
• ACL Anthology: https://aclanthology.org/2024.findings-acl.236/
• arXiv: https://arxiv.org/abs/2407.01948

Citation

If you use T5FactExtractor, please cite:

@inproceedings{messina-etal-2024-extracting,
    title = "Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation",
    author = "Messina, Pablo  and
      Vidal, Rene  and
      Parra, Denis  and
      Soto, Alvaro  and
      Araujo, Vladimir",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.236/",
    doi = "10.18653/v1/2024.findings-acl.236",
    pages = "3955--3986"
}