  Is it possible to develop an "AI Pathologist" to pass the board-certified
examination of the American Board of Pathology? To achieve this goal, the first
step is to create a visual question answering (VQA) dataset where the AI agent
is presented with a pathology image together with a question and is asked to
give the correct answer. Our work makes the first attempt to build such a
dataset. Different from creating general-domain VQA datasets where the images
are widely accessible and there are many crowdsourcing workers available and
capable of generating question-answer pairs, developing a medical VQA dataset
is much more challenging. First, due to privacy concerns, pathology images are
usually not publicly available. Second, only well-trained pathologists can
understand pathology images, but they barely have time to help create datasets
for AI research. To address these challenges, we resort to pathology textbooks
and online digital libraries. We develop a semi-automated pipeline to extract
pathology images and captions from textbooks and generate question-answer pairs
from captions using natural language processing. We collect 32,799 open-ended
questions from 4,998 pathology images where each question is manually checked
to ensure correctness. To our best knowledge, this is the first dataset for
pathology VQA. Our dataset will be released publicly to promote research in
medical VQA.
