# Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis

arXiv: https://arxiv.org/abs/2406.15050 · HTML: https://mlsanity.com/paper/2406.15050/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering](https://mlsanity.com/paper/2102.09542/) (2021)
- [Bilinear Attention Networks](https://mlsanity.com/paper/1805.07932/) (2018)
- [Stacked Attention Networks for Image Question Answering](https://mlsanity.com/paper/1511.02274/) (2015)
- [Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding](https://mlsanity.com/paper/1606.01847/) (2016)
- [On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering](https://mlsanity.com/paper/2002.10215/) (2020)
- [Overcoming Data Limitation in Medical Visual Question Answering](https://mlsanity.com/paper/1909.11867/) (2019)
- [Multimodal Residual Learning for Visual QA](https://mlsanity.com/paper/1606.01455/) (2016)
- [Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering](https://mlsanity.com/paper/2307.05314/) (2023)
- [Cross-Modal Self-Attention with Multi-Task Pre-Training for Medical Visual Question Answering](https://mlsanity.com/paper/2105.00136/) (2021)
- [A Question-Centric Model for Visual Question Answering in Medical Imaging](https://mlsanity.com/paper/2003.08760/) (2020)
- [Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images](https://mlsanity.com/paper/2207.04219/) (2022)
- [Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks](https://mlsanity.com/paper/2110.06367/) (2021)

## Built on by (most cited first, 1 of 1)

- [Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering](https://mlsanity.com/paper/2603.13878/) (2026)

## Abstract (arXiv)

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.