metadata

datasets:
  - StanfordAIMI/interpret-cxr-test-public
  - StanfordAIMI/interpret-cxr-test-hidden
library_name: transformers
license: apache-2.0

CXRMate-RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation

This is an evolution of https://huggingface.co/aehrc/cxrmate developed for the Radiology Report Generation task of BioNLP @ ACL 2024.

The leaderboard for the task can be found here.

For this, we proposed EAST: Entropy-Augmented Self-critical sequence Training (EAST):

EAST modifies Self-Critical Sequence Training (SCST) by adding entropy regularisation.
Helps maintain a higher entropy in the token distribution.
Preventing overfitting to common phrases and ensuring a broader exploration of the vocabulary during training.
This was essential to handle the diversity of the radiology reports in the RRG24 datasets.

EAST was applied to a multimodal language model with RadGraph as the reward. Other features include:

Token type embeddings to differentiate between findings and impression section tokens, as well as image embeddings.
Special tokens ([NF] and [NI]) to handle missing findings and impression sections.
Non-causal attention masking for the image embeddings and a causal attention masking for the report token embeddings.

Example:

import torch  
from torchvision.transforms import v2
import transformers


tokenizer = transformers.AutoTokenizer.from_pretrained('aehrc/cxrmate-rrg24')
model = transformers.AutoModel.from_pretrained('aehrc/cxrmate-rrg24', trust_remote_code=True)
transforms = v2.Compose(
    [
        v2.PILToTensor(),
        v2.Grayscale(num_output_channels=3),
        v2.Resize(size=model.config.encoder.image_size, antialias=True),
        v2.CenterCrop(size=[model.config.encoder.image_size]*2),
        v2.ToDtype(torch.float32, scale=True),
        v2.Normalize(mean=model.config.encoder.image_mean, std=model.config.encoder.image_std),
    ]
)

dataset = datasets.load_dataset('StanfordAIMI/interpret-cxr-test-public')['test']

def transform_batch(batch):
    batch['images'] = [torch.stack([transforms(j) for j in i]) for i in batch['images']]
    batch['images'] = torch.nn.utils.rnn.pad_sequence(batch['images'], batch_first=True, padding_value=0.0)  
    return batch

dataset = dataset.with_transform(transform_batch)
dataloader = DataLoader(dataset, batch_size=mbatch_size, shuffle=True)
batch = next(iter(dataloader))

output_ids = model.generate(
    pixel_values=batch['images'],
    max_length=512,
    num_beams=4,
    bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NF]')], [tokenizer.convert_tokens_to_ids('[NI]')]],
)
findings, impression = model.split_and_decode_sections(output_ids, tokenizer)

Generate findings only:

output_ids = model.generate(
    pixel_values=batch['images'],
    max_length=512,
    num_beams=4,
    bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NF]')]],
    eos_token_id=tokenizer.sep_token_id,
)
findings, _ = model.split_and_decode_sections(output_ids, tokenizer)

Generate impression only:

output_ids = model.generate(
    pixel_values=batch['images'],
    max_length=512,
    num_beams=4,
    bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NI]')]],
    input_ids=torch.tensor([[tokenizer.bos_token_id, tokenizer.convert_tokens_to_ids('[NF]'), tokenizer.sep_token_id]]*mbatch_size, device=device, dtype=torch.long),
)
_, impression = model.split_and_decode_sections(output_ids, tokenizer)

Notebook example:

https://huggingface.co/aehrc/cxrmate-rrg24/blob/main/demo.ipynb

Citation:

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