This repo demonstrates the efforts made for the ImageCLEF 2019 VQA-Med Q&A challenge.

As sepcified in the ImageClef site, the input for the model is constructed of an image + natural language question about said image, and an asnwer to the question.
The task is to predict the answer for a similar data which the answer was ommitted from.

You can see the the outline of the work done:

1. (bringing data to expected format)[https://github.com/turner11/VQA-MED/blob/master/VQA-MED/VQA.Python/0_bringing_data_to_expected_format.ipynb]
2. Pre process data (Clean + Enrich data)
3. Data augmentation
4. Create meta data
5. Create the model
6. Train the model
7. Predict
8. Create a submission in the expected format

For more information, please read the following paper:

Avi Turner, Assaf Spanier. "LSTM in VQA-Med, is It Really Needed? JCE Study on the ImageCLEF 2019 Dataset." CLEF (Working Notes) 2019

Please also cite this paper if you are using it for your research!