2019/05/28 by Tomasz Kornuta, Kornuta, Tomasz, Deepta Rajan +8 · 9 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Architecture #Artificial Intelligence (cs.AI) #Artificial intelligence #Competition (biology) #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #IBM #Information retrieval #Machine Learning (cs.LG) #Machine learning #Modular design #Multimodal Machine Learning Applications #Operating system #Pipeline (software) #Point (geometry) #Question answering #Set (abstract data type) #Task (project management) #Transfer of learning #cs.AI #cs.CL #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1905.12008
published in arXiv (Cornell University) (Cornell University) · Working notes from the ImageCLEF 2019 VQA-Med competition
arxiv created 2019/05/28 · openalex publication_date 2019/05/28 · arxiv updated 2019/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.