This repository is the implementation of Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering for the visual question answering task. Our single model achieved 70.93 (Test-standard, VQA 2.0). Moreover, in TDIUC dataset, our single model achieved 73.04 in Arithmetic MTP metric and 66.86 in Harmonic MTP metric.

Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the medical domain explores approaches to this form of clinical decision support. Success of such machine learning tools hinges on availability and design of collections composed of medical images augmented with question-answer pairs directed at the content of the image. We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers. Manual categorization of images and questions provides insight into clinically relevant tasks and the natural language to phrase them.