Visual Question Answering (VQA) on Images with Superimposed Text
This validates the practice of superimposing text on images for VQA in healthcare, though it is incremental as it focuses on a specific domain challenge.
The study investigated the effect of superimposed text on medical images for Visual Question Answering (VQA), finding that adding textual meta-information does not severely degrade key VQA performance measures.
Superimposed text annotations have been under-investigated, yet are ubiquitous, useful and important, especially in medical images. Medical images also highlight the challenges posed by low resolution, noise and superimposed textual meta-information. Therefor we probed the impact of superimposing text onto medical images on VQA. Our results revealed that this textual meta-information can be added without severely degrading key measures of VQA performance. Our findings are significant because they validate the practice of superimposing text on images, even for medical images subjected to the VQA task using AI techniques. The work helps advance understanding of VQA in general and, in particular, in the domain of healthcare and medicine.