Intelligent data extraction from medical reports

From a medical repport image (in french) not template based the system extract the following informations using NER and image segmentation:

• the patient’s name, and date of birth
• the date of the medical intervention
• the type of medical intervention (for example : radiology)
• the name of the doctor who performed the medical intervention
• the address of the intervention
• the referring doctor

The system work as follow :

Requirements

• Tesseract 5.0.0
• pytesseract 0.3.8
• NumPy 1.19.5
• OpenCV python 4.5.1.48
• SpaCy 3.2.0

Image segmentation and text extraction

The image segmentation and text extraction from image of medical repport is done using the algorithm ACABS (Automatic Cropper and Block Segmenter). For more details about ACABS see the repport.

ACABS

ACABS first detect and segment the image into block of text. It then select the relevent block text and remove the report's header and footer. Finally the text is extracted from the medical image report thanks to OCR.

from acabs import ACABS

#usage on an image
text = ACABS(path_to_image)

#usage on a folder of image
import os

texts = ''
_, _, filenames = next(os.walk(path_to_folder))
os.chdir(path_to_folder)
for file in filenames:
    text = ACABS(file)
        with open('path_to_save_text/' + file.split('.')[0] + '.txt', 'w') as f:
        f.write(text)

    texts = texts + '\jump=================== New repport : ' + file + ' ===================\jump' + text

Visual output of ACABS segmentation:

Extraction of key information

After using ACABS to extract the text, the data need to be annotated (like annotated_text.json). In order to speed up the annotation use a software like ner-annotator.

Transform the data into a spacy like format using transform_data.py.

Train a model

Use config.cfg to customize the NER model.

• Verify the data:

python -m spacy debug data ./config.cfg --paths.train ./train.spacy --paths.dev ./dev.spacy

• Train the model:

python -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ../dev.spacy --gpu-id 1

• Evaluate the model:

python -m spacy evaluate ./output/model-best ./dev.spacy

Use a model

See predictions.ipynb