README.md

metadata

license: mit
task_categories:
  - feature-extraction
language:
  - en
tags:
  - medical
size_categories:
  - 10K<n<100K
pretty_name: MedMKG

RADM: Radiological Multimodal Knowledge Graph

We introduce RADM, a a Radiological Multimodal Knowledge Graph that seamlessly fuses clinical concepts with medical images.
MedMKG is constructed via a multi-stage pipeline that accurately identifies and disambiguates medical concepts while extracting their interrelations.
To ensure the conciseness of the resulting graph, we further employ a pruning strategy based on our novel Neighbor-aware Filtering (NaF) algorithm.

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📂 Provided Files

This repository contains:

• RADM.csv — biomedical triplets: Head, Relation, Tail, Head_Name, Tail_Name
• image_mapping.csv — image ID to relative path mappings

Note: The images themselves are not included. Users must download MIMIC-CXR-JPG separately and specify their local path.

📦 About MIMIC-CXR-JPG

MIMIC-CXR-JPG is a large publicly available dataset of chest radiographs in JPEG format, sourced from the Beth Israel Deaconess Medical Center in Boston.

• URL: https://physionet.org/content/mimic-cxr-jpg/2.1.0/
• Total uncompressed size: 570.3 GB

Access Instructions

To use the image data, you must request access and agree to the data use agreement, which includes:

1. You will not share the data.
2. You will not attempt to reidentify individuals.
3. Any publication using the data will make the relevant code available.

Download options:

• ZIP download

• Google BigQuery access

• Google Cloud Storage Browser access

• Command-line download:

  wget -r -N -c -np --user your_username --ask-password https://physionet.org/files/mimic-cxr-jpg/2.1.0/
  

🔧 Usage Example

Below is a demo script to load and link the knowledge graph with your local image data:

import pandas as pd

kg_path = "RADM.csv"
mapping_path = "image_mapping.csv"

# Load CSVs
kg_df = pd.read_csv(kg_path)
mapping_df = pd.read_csv(mapping_path)

# Local path to downloaded MIMIC-CXR images
local_root = "/path/to/your/mimic-cxr-jpg"

# Map image IDs to full paths
iid_to_path = {
    row["IID"]: f"{local_root}/{row['Image_Path']}"
    for _, row in mapping_df.iterrows()
}

# Merge image path info into KG
kg_df["Head_Path"] = kg_df["Head"].map(iid_to_path)
kg_df["Tail_Path"] = kg_df["Tail"].map(iid_to_path)

print(kg_df.head())

Benchmark Instruction

We cover codes for three downstream tasks detailed in our paper, including link prediction, knowledge-augmented visual question answering, and knowledge-augmented text-image retrieval. You may check the three folders and execute the following:

python main.py

after specifying your local paths of data files.