This model is trained to serve the RaTEScore metric, if you are interested in our pipeline, please refer to our
paper
and
Github
.
This model also can be used to extract
Abnormality, Non-Abnormality, Anatomy, Disease, Non-Disease
in medical radiology reports.
Usage
Click to expand the usage of this model.
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
def post_process(tokenized_text, predicted_entities, tokenizer):
entity_spans = []
start = end = None
entity_type = None
for i, (token, label) in enumerate(zip(tokenized_text, predicted_entities[:len(tokenized_text)])):
if token in ["[CLS]", "[SEP]"]:
continue
if label != "O" and i < len(predicted_entities) - 1:
if label.startswith("B-") and predicted_entities[i+1].startswith("I-"):
start = i
entity_type = label[2:]
elif label.startswith("B-") and predicted_entities[i+1].startswith("B-"):
start = i
end = i
entity_spans.append((start, end, label[2:]))
start = i
entity_type = label[2:]
elif label.startswith("B-") and predicted_entities[i+1].startswith("O"):
start = i
end = i
entity_spans.append((start, end, label[2:]))
start = end = None
entity_type = None
elif label.startswith("I-") and predicted_entities[i+1].startswith("B-"):
end = i
if start is not None:
entity_spans.append((start, end, entity_type))
start = i
entity_type = label[2:]
elif label.startswith("I-") and predicted_entities[i+1].startswith("O"):
end = i
if start is not None:
entity_spans.append((start, end, entity_type))
start = end = None
entity_type = None
if start is not None and end is None:
end = len(tokenized_text) - 2
entity_spans.append((start, end, entity_type))
save_pair = []
for start, end, entity_type in entity_spans:
entity_str = tokenizer.convert_tokens_to_string(tokenized_text[start:end+1])
save_pair.append((entity_str, entity_type))
return save_pair
def run_ner(texts, idx2label, tokenizer, model, device):
inputs = tokenizer(texts,
max_length=512,
padding=True,
truncation=True,
return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
predicted_labels = torch.argmax(outputs.logits, dim=2).tolist()
save_pairs = []
for i in range(len(texts)):
predicted_entities = [idx2label[label] for label in predicted_labels[i]]
non_pad_mask = inputs["input_ids"][i] != tokenizer.pad_token_id
non_pad_length = non_pad_mask.sum().item()
non_pad_input_ids = inputs["input_ids"][i][:non_pad_length]
tokenized_text = tokenizer.convert_ids_to_tokens(non_pad_input_ids)
save_pair = post_process(tokenized_text, predicted_entities, tokenizer)
if i == 0:
save_pairs = save_pair
else:
save_pairs.extend(save_pair)
return save_pairs
ner_labels = ['B-ABNORMALITY', 'I-ABNORMALITY',
'B-NON-ABNORMALITY', 'I-NON-ABNORMALITY',
'B-DISEASE', 'I-DISEASE',
'B-NON-DISEASE', 'I-NON-DISEASE',
'B-ANATOMY', 'I-ANATOMY',
'O']
idx2label = {i: label for i, label in enumerate(ner_labels)}
tokenizer = AutoTokenizer.from_pretrained('Angelakeke/RaTE-NER-Deberta')
model = AutoModelForTokenClassification.from_pretrained('Angelakeke/RaTE-NER-Deberta')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
If you have any questions, please feel free to contact
[email protected]
.
Citation
@article{zhao2024ratescore,
title={RaTEScore: A Metric for Radiology Report Generation},
author={Zhao, Weike and Wu, Chaoyi and Zhang, Xiaoman and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
journal={arXiv preprint arXiv:2406.16845},
year={2024}
}
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