  Visual question answering (VQA) in surgery is largely unexplored. Expert
surgeons are scarce and are often overloaded with clinical and academic
workloads. This overload often limits their time answering questionnaires from
patients, medical students or junior residents related to surgical procedures.
At times, students and junior residents also refrain from asking too many
questions during classes to reduce disruption. While computer-aided simulators
and recording of past surgical procedures have been made available for them to
observe and improve their skills, they still hugely rely on medical experts to
answer their questions. Having a Surgical-VQA system as a reliable 'second
opinion' could act as a backup and ease the load on the medical experts in
answering these questions. The lack of annotated medical data and the presence
of domain-specific terms has limited the exploration of VQA for surgical
procedures. In this work, we design a Surgical-VQA task that answers
questionnaires on surgical procedures based on the surgical scene. Extending
the MICCAI endoscopic vision challenge 2018 dataset and workflow recognition
dataset further, we introduce two Surgical-VQA datasets with classification and
sentence-based answers. To perform Surgical-VQA, we employ vision-text
transformers models. We further introduce a residual MLP-based VisualBert
encoder model that enforces interaction between visual and text tokens,
improving performance in classification-based answering. Furthermore, we study
the influence of the number of input image patches and temporal visual features
on the model performance in both classification and sentence-based answering.
