RATCHET is a Medical Transformer for Chest X-ray Diagnosis and Reporting

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Deep Learning RATCHET
Overview

RATCHET: RAdiological Text Captioning for Human Examined Thoraxes

RATCHET is a Medical Transformer for Chest X-ray Diagnosis and Reporting. Based on the architecture featured in Attention Is All You Need. This network is trained and validated on the MIMIC-CXR v2.0.0 dataset.

Architecture

RATCHET Architecture

Run the code

Download pretrained weights (v1, v2) and put in ./checkpoints folder. Then run:

streamlit run web_demo.py
Environment:
Python 3.7.4
Packages:
imageio                  2.8.0
matplotlib               3.2.1
numpy                    1.18.4
pandas                   1.0.3
scikit-image             0.17.2
streamlit                0.67.1
tensorflow-gpu           2.3.0
tokenizers               0.7.0
tqdm                     4.46.0

Results

     Cardiomegaly           Cardiomegaly Attention Plot     

Generated Text:

In comparison with the study of ___, there is little overall change. Again there is substantial enlargement of the cardiac silhouette with a dual-channel pacer device in place. No evidence of vascular congestion or acute focal pneumonia. Blunting of the costophrenic angles is again seen.

More Examples

More Captioning Examples

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Comments
  • How to train or evaluate the model?

    How to train or evaluate the model?

    Considering the training and evaluating the model, how can it be done? I tried to evaluate the model using python3 evaluate.py but it looks like there is a missing file

    FileNotFoundError: [Errno 2] No such file or directory: 'preprocessing/mimic/MIMIC_AP_PA_test.csv'

    opened by Minashraf 2
  • Training the model using 10% of the dataset

    Training the model using 10% of the dataset

    @farrell236 Hey farrell, Since the dataset is too large can I train the model using only 10% of the dataset? (Currently I don't have enough computational power and other resources to train the model with whole MIMIC-CXR dataset of size ~500GB). If that is possible what are the steps, I need to follow to successfully train the model with only 10% of the dataset. As far as I understand, I may need to,

    • Create the MIMIC_AP_PA_train.csv with only 10% of the original train dataset
    • Create both mimic-merges.txt & mimic-vocab.json from the (10% train + test + validate) data

    Other than these do I have to follow any other steps?

    Also, instead of DenseNet-121 can't we use something like MobileNetV2 for the encoder?

    opened by Wimukti 0
Owner
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