VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2019

Asma Ben Abacha, Sadid A. Hasan, Vivek V. Datla, Joey Liu, Dina Demner-Fushman, Henning Müller
2019 Conference and Labs of the Evaluation Forum  
This paper presents an overview of the Medical Visual Question Answering task (VQA-Med) at ImageCLEF 2019. Participating systems were tasked with answering medical questions based on the visual content of radiology images. In this second edition of VQA-Med, we focused on four categories of clinical questions: Modality, Plane, Organ System, and Abnormality. These categories are designed with different degrees of difficulty leveraging both classification and text generation approaches. We also
more » ... ured that all questions can be answered from the image content without requiring additional medical knowledge or domain-specific inference. We created a new dataset of 4,200 radiology images and 15,292 question-answer pairs following these guidelines. The challenge was well received with 17 participating teams who applied a wide range of approaches such as transfer learning, multi-task learning, and ensemble methods. The best team achieved a BLEU score of 64.4% and an accuracy of 62.4%. In future editions, we will consider designing more goal-oriented datasets and tackling new aspects such as contextual information and domain-specific inference.
dblp:conf/clef/AbachaHDLDM19 fatcat:gpu5qdvcvzgdnpfwp2is5i7gwa