Update README.md

README.md

@@ -27,4 +27,102 @@ This is the model and data pipeline for the CXRMate-ED model from: https://arxiv
 
 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 aperiodic vital signs, medications, 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, significantly enhancing 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."
+"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 aperiodic vital signs, medications, 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, significantly enhancing 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."
+
+## Example
+
+```python
+import torch
+import transformers
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from torch.utils.data import DataLoader
+from torchvision.transforms import v2
+import os
+import pprint
+import matplotlib.pyplot as plt
+from torchvision.utils import make_grid
+
+# Device and paths:
+device = 'cuda'
+physionet_dir = '/datasets/work/hb-mlaifsp-mm/work/archive/physionet.org/files'
+dataset_dir = '/scratch3/nic261/datasets'
+database_path = '/scratch3/nic261/database/cxrmated_test.db'
+mimic_cxr_jpg_dir = '/scratch3/nic261/datasets/physionet.org/files/mimic-cxr-jpg/2.0.0/files'
+
+# Download model checkpoint:
+model = transformers.AutoModel.from_pretrained('aehrc/cxrmate-ed', trust_remote_code=True).to(device=device)
+model.eval()
+
+# Download tokenizer:
+tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained('aehrc/cxrmate-ed')
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+# Image transforms:
+image_size = 384
+test_transforms = v2.Compose(
+    [
+        v2.Grayscale(num_output_channels=3),
+        v2.Resize(
+            size=image_size, 
+            antialias=True,
+            interpolation=v2.InterpolationMode.BICUBIC,
+        ),
+        v2.CenterCrop(size=[image_size, image_size]),
+        v2.ToDtype(torch.float32, scale=True),
+        v2.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    ]
+)
+
+# Prepare the MIMIC-CXR & MIMIC-IV-ED dataset:
+model.prepare_data(
+    physionet_dir=physionet_dir,
+    dataset_dir=dataset_dir,
+    database_path=database_path,
+)
+
+# Get the test set dataset & dataloader:
+test_set = model.get_dataset('test', test_transforms, database_path, mimic_cxr_jpg_dir)
+test_dataloader = DataLoader(
+    test_set,
+    batch_size=1, 
+    num_workers=5,
+    shuffle=True,
+    collate_fn=model.collate_fn,
+    pin_memory=True,
+)
+
+# Get an example:
+batch = next(iter(test_dataloader))
+
+# Move tensors in the batch to the device:
+for key, value in batch.items():
+    if isinstance(value, torch.Tensor):
+        batch[key] = value.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, **batch)
+    
+# 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],
+    token_type_id_sections=model.decoder.config.section_ids,
+    max_length=256,
+    bos_token_id=tokenizer.bos_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id,
+    num_beams=4,
+    return_dict_in_generate=True,
+    use_cache=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')
+
+```