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arXiv · 1905.12008

Leveraging Medical Visual Question Answering with Supporting Facts

Tomasz Kornuta·Deepta Rajan·Chaitanya Shivade·Alexis Asseman·Ahmet S. Ozcan
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Abstract

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.

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BibTeXRIS

Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S. Ozcan. 2019-05-28. Leveraging Medical Visual Question Answering with Supporting Facts. https://arxiv.org/abs/1905.12008

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