In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition. The challenge consists of four question-answering tasks based on radiology images. The diversity of imaging modalities, organs and disease types combined with a small imbalanced training set made this a highly complex problem. To overcome these difficulties, we implemented a modular pipeline architecture that utilized transfer learning and multi-task learning. Our findings led to the development of a novel model called Supporting Facts Network (SFN). The main idea behind SFN is to cross-utilize information from upstream tasks to improve the accuracy on harder downstream ones. This approach significantly improved the scores achieved in the validation set (18 point improvement in F-1 score). Finally, we submitted four runs to the competition and were ranked seventh.
@article{arxiv.1905.12008,
title = {Leveraging Medical Visual Question Answering with Supporting Facts},
author = {Tomasz Kornuta and Deepta Rajan and Chaitanya Shivade and Alexis Asseman and Ahmet S. Ozcan},
journal= {arXiv preprint arXiv:1905.12008},
year = {2019}
}
Comments
Working notes from the ImageCLEF 2019 VQA-Med competition