What is it about?
Medical Visual Question Answering (VQA-Med) is a challenging task that involves answering clinical questions related to medical images. VQA-Med technology can assist doctors in improving the diagnosis efficiency and help patients understand their conditions. To explore the interpretability of Medical Visual Question Answering (VQA-Med), this paper proposes a novel model for VQA-Med based on a counterfactual causal-effect intervention strategy and structural causal reasoning. Our open codes is available at https://github.com/S200331082/Counterfactual_MVQA.git
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Why is it important?
Interpretability is critical to producing convincing answers for the reliability and trustworthiness of VQA-Med to help doctors understand patients comprehensively and make correct and appropriate clinical decisions. Hoverver, most current VQA-Med methods ignore the causal correlation between specific lesion or abnormality features and answers, while also failing to provide accurate explanations for their decisions. The proposed CCIS-MVQA in this paper can enhance interpretability and generalization.
Perspectives
First, we propose a novel counterfactual causal-effect intervention strategy (CCIS ) framework for VQA-Med (CCIS-MVQA) to explore the interpretability , which integrates an interpretability generator into its architecture to provide interpretations and explanations for its predictions. Second, we develop layer-wise relevance propagation to automatically generate counterfactual image samples and construct a novel counterfactual causal-effect intervention strategy, which can simultaneously produce interpretability and prediction results. Third, we perform extensive experiments on benchmark datasets. Our proposed CCIS-MVQA achieves new SOTA results compared with the existing methods in VQA-Med fields. Additionally, we provide sufficient visualization results to analyze the interpretability and debasing performance.
Prof. Linqin CAI
Read the Original
This page is a summary of: Counterfactual Causal-Effect Intervention for Interpretable Medical Visual Question Answering, IEEE Transactions on Medical Imaging, January 2024, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tmi.2024.3425533.
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