  The intersection of medical Visual Question Answering (Med-VQA) is a
challenging research topic with advantages including patient engagement and
clinical expert involvement for second opinions. However, existing Med-VQA
methods based on joint embedding fail to explain whether their provided results
are based on correct reasoning or coincidental answers, which undermines the
credibility of VQA answers. In this paper, we investigate the construction of a
more cohesive and stable Med-VQA structure. Motivated by causal effect, we
propose a novel Triangular Reasoning VQA (Tri-VQA) framework, which constructs
reverse causal questions from the perspective of "Why this answer?" to
elucidate the source of the answer and stimulate more reasonable forward
reasoning processes. We evaluate our method on the Endoscopic Ultrasound (EUS)
multi-attribute annotated dataset from five centers, and test it on medical VQA
datasets. Experimental results demonstrate the superiority of our approach over
existing methods. Our codes and pre-trained models are available at
https://anonymous.4open.science/r/Tri_VQA.
