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README.md

Docs

Install uv: "An extremely fast Python package manager".

curl -LsSf https://astral.sh/uv/install.sh | sh

Create venv

cd ~/cdi-hub/tutorials/automatic-medical-image-reporting
uv venv --python 3.12 # Create a virtual environment at .venv.

Acvivate venv

source .venv/bin/activate #To activate the virtual environment:
deactivate

Install python package deps

uv pip install --editable ".[test, learning]" # Install the package in editable mode with test and learning dependencies
uv pip install ."[learning]" # Install learning dependencies

Clean up any existing installation and reinstall:

uv pip uninstall ammir
uv pip install -e ".[test,learning]"

References

Click to see references
  • [RATCHET-2021],
  • [BioBERT-2020]
  • Train and evaluate Medical Transformer for Chest X-ray Diagnosis and Reporting [7-9].
  • Implement evaluation tools for image captioning, including BLEU, ROUGE-L, CIDEr, METEOR, SPICE scores [10].
  • Developoing a python-based interface using either Streamlit for a web-based solution or a simple command-line interface with Click or another suitable tool.

  1. Guo, Li, Anas M. Tahir, Dong Zhang, Z. Jane Wang, and Rabab K. Ward. "Automatic Medical Report Generation: Methods and Applications." APSIPA Transactions on Signal and Information Processing 13, no. 1 (2024). DOI arxiv

  2. Hou, Benjamin, Georgios Kaissis, Ronald M. Summers, and Bernhard Kainz. "Ratchet: Medical transformer for chest x-ray diagnosis and reporting." In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VII 24, pp. 293-303. Springer International Publishing, 2021. arxiv google-citations

  3. Ramesh, Vignav, Nathan A. Chi, and Pranav Rajpurkar. "Improving radiology report generation systems by removing hallucinated references to non-existent priors." In Machine Learning for Health, pp. 456-473. PMLR, 2022. arxiv github-repo google-scholar

  4. https://physionet.org/content/mimic-cxr-jpg/2.1.0/

4.1. https://github.com/filipepcampos/mimic-cxr-jpg-loader

4.2. "Training a Convolutional Neural Network to Classify Chest X-rays" https://github.com/MIT-LCP/2019-hst-953/blob/master/tutorials/mimic-cxr/mimic-cxr-train.ipynb

4.3. "Predict plueral effusion in chest x-rays using the MIMIC-CXR-JPG dataset" https://github.com/dalton-macs/pleural-effusion-cnn/tree/main/data

  1. https://physionet.org/content/mimic-cxr/2.1.0/

5.1 https://mimic.mit.edu/docs/iv/modules/cxr/

  1. https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university

6.1 https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university/code

  1. https://github.com/omar-mohamed/X-Ray-Report-Generation/

  2. https://doi.org/10.1016/j.imu.2021.100557

  3. https://github.com/farrell236/RATCHET

  4. https://github.com/Aldenhovel/bleu-rouge-meteor-cider-spice-eval4imagecaption