A dataset of clinically generated visual questions and answers about radiology images

• Published in: Scientific data Vol. 5; no. 1; pp. 180251 - 10
• Main Authors: Lau, Jason J., Gayen, Soumya, Ben Abacha, Asma, Demner-Fushman, Dina
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.11.2018; Nature Publishing Group
• Subjects: 631/114/2164; 692/700/1421/1770; Algorithms; Artificial intelligence; Cancer; Data Analysis; Data collection; Data Descriptor; Data Mining; Datasets; Decision making; Decision support systems; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine Learning; multidisciplinary; Radiography - methods; Radiology Information Systems - classification; Radiology Information Systems - standards; Science

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 Answe...