README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- chest X-ray report generation
- radiology report generation
- image captioning
- chest X-ray
- X-ray
- radiology
- cxrmate
- cxrmate-ed
- report
- radiology report
- multimodal
- patient data
- patient records
- mimic-cxr
- mimic-iv-ed
---

# **CXRMate-ED**: The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It

This is the model and data pipeline for the CXRMate-ED model from: https://arxiv.org/pdf/2406.13181.

The abstract from the paper:

"This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Traditionally, CXR report generation relies solely on CXR images and limited radiology data, overlooking valuable information from patient health records, particularly from emergency departments. Utilising the MIMIC-CXR and MIMIC-IV-ED datasets, we incorporate detailed patient information such as vital signs, medicines, and clinical history to enhance diagnostic accuracy. We introduce a novel approach to transform these heterogeneous data sources into embeddings that prompt a multimodal language model; this significantly enhances the diagnostic accuracy of generated radiology reports. Our comprehensive evaluation demonstrates the benefits of using a broader set of patient data, underscoring the potential for enhanced diagnostic capabilities and better patient outcomes through the integration of multimodal data in CXR report generation."

### Prepare the dataset:

```python
import transformers

# Paths:
physionet_dir = '/.../physionet.org/files'  # Where MIMIC-CXR, MIMIC-CXR-JPG, and MIMIC-IV-ED are stored.
database_dir = '/.../database/cxrmate_ed'  # The Hugging Face dataset will be saved here.

# Prepare the Hugging Face MIMIC-CXR & MIMIC-IV-ED dataset:
model = transformers.AutoModel.from_pretrained('aehrc/cxrmate-ed', trust_remote_code=True)
model.prepare_data(physionet_dir=physionet_dir, database_dir=database_dir)
```

## Generate a report

```python
import torch
import transformers

# Device and paths:
device = 'cuda'

# Download model checkpoint:
model = transformers.AutoModelForCausalLM.from_pretrained('aehrc/cxrmate-ed', trust_remote_code=True).to(device=device)
tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained('aehrc/cxrmate-ed')

# Get the Hugging Face MIMIC-CXR & MIMIC-IV-ED test set:
test_set = model.get_dataset(database_dir=database_dir, test_set_only=True)

# Get an example, add mini-batch dimension and move to device:
example = test_set[0]
example = {k: v.to(device).unsqueeze(0) if isinstance(v, torch.Tensor) else [v] for k, v in example.items()}  # Add mini-batch dimension and move to device.

# Convert the patient data in the batch into embeddings:
inputs_embeds, attention_mask, token_type_ids, position_ids, bos_token_ids = model.prepare_inputs(tokenizer=tokenizer, **example)
    
# Generate reports:
output_ids = model.generate(
    input_ids=bos_token_ids,
    decoder_inputs_embeds=inputs_embeds,
    decoder_token_type_ids=token_type_ids,
    prompt_attention_mask=attention_mask,
    prompt_position_ids=position_ids,
    special_token_ids=[tokenizer.sep_token_id],
    max_length=256,
    num_beams=4,
    return_dict_in_generate=True,
)['sequences']

# Findings and impression section:
findings, impression = model.split_and_decode_sections(output_ids, [tokenizer.sep_token_id, tokenizer.eos_token_id], tokenizer)
for i,j in zip(findings, impression):
    print(f'Findings:\t{i}\nImpression:\t{j}\n\n')
```

## MIMIC-CXR & MIMIC-IV-ED Dataset:

MIMIC-CXR, MIMIC-CXR-JPG, and MIMIC-IV-ED must be in the same Physio Net directory. E.g.:

```shell
user@cluster:~$ ls /home/user/physionet.org/files
mimic-cxr  mimic-cxr-jpg  mimic-iv-ed
```

### Download MIMIC-CXR-JPG:

Download the MIMIC-CXR-JPG dataset from https://physionet.org/content/mimic-cxr-jpg, e.g.,
```shell
wget -r -N -c -np --user <username> --ask-password https://physionet.org/files/mimic-cxr-jpg/2.1.0/
```
Note that you must be a credentialised user to access this dataset.

### Download the reports from MIMIC-CXR:

MIMIC-CXR-JPG does not include the radiology reports and are instead included with MIMIC-CXR (the DICOM version of the dataset). To download this dataset and avoid downloading the DICOM files (which are very large), use `--reject dcm` with the wget command from https://physionet.org/content/mimic-cxr, e.g, 
```shell
wget -r -N -c -np --reject dcm --user <username> --ask-password https://physionet.org/files/mimic-cxr/2.0.0/
```
Note that you must be a credentialised user to access this dataset.

### Download MIMIC-IV-ED:

Download the MIMIC-IV-ED dataset from https://physionet.org/content/mimic-iv-ed, e.g.,
```shell
wget -r -N -c -np --user <username> --ask-password https://physionet.org/files/mimic-iv-ed/2.2/
```
Note that you must be a credentialised user to access this dataset.

# Environment requirements

Environment requirements can be found here: https://github.com/aehrc/cxrmate-ed/blob/main/requirements.txt.


# Code repository

The code repository, which includes the training pipeline for CXRMate-ED, is available at: https://github.com/aehrc/cxrmate-ed.