  Is it possible to develop an "AI Pathologist" to pass the board-certified
examination of the American Board of Pathology (ABP)? To build such a system,
three challenges need to be addressed. First, we need 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. Due to privacy concerns, pathology images are usually not publicly
available. Besides, only well-trained pathologists can understand pathology
images, but they barely have time to help create datasets for AI research. The
second challenge is: since it is difficult to hire highly experienced
pathologists to create pathology visual questions and answers, the resulting
pathology VQA dataset may contain errors. Training pathology VQA models using
these noisy or even erroneous data will lead to problematic models that cannot
generalize well on unseen images. The third challenge is: the medical concepts
and knowledge covered in pathology question-answer (QA) pairs are very diverse
while the number of QA pairs available for modeling training is limited. How to
learn effective representations of diverse medical concepts based on limited
data is technically demanding. In this paper, we aim to address these three
challenges. To our best knowledge, our work represents the first one addressing
the pathology VQA problem. To deal with the issue that a publicly available
pathology VQA dataset is lacking, we create PathVQA dataset. To address the
second challenge, we propose a learning-by-ignoring approach. To address the
third challenge, we propose to use cross-modal self-supervised learning. We
perform experiments on our created PathVQA dataset and the results demonstrate
the effectiveness of our proposed learning-by-ignoring method and cross-modal
self-supervised learning methods.
