IEEE transactions on medical imaging. 2024 Jul 9:PP. doi: 10.1109/TMI.2024.3425533 Q19.82025

Counterfactual Causal-Effect Intervention for Interpretable Medical Visual Question Answering

一种用于可解释的医学视觉问答的反事实因果效应干预方法翻译改进

Linqin Cai, Haodu Fang, Nuoying Xu, Bo Ren

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DOI: 10.1109/TMI.2024.3425533 PMID: 38980786

Medical Visual Question Answering (VQA-Med) is a challenging task that involves answering clinical questions related to medical images. However, 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. To explore the interpretability of VQA-Med, this paper proposes a novel CCIS-MVQA model for VQA-Med based on a counterfact... ...点击完成人机验证后继续浏览

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期刊名：Ieee transactions on medical imaging

缩写：IEEE T MED IMAGING

ISSN：0278-0062

e-ISSN：1558-254X

IF/分区：9.8/Q1

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Counterfactual Causal-Effect Intervention for Interpretable Medical Visual Question Answering

一种用于可解释的医学视觉问答的反事实因果效应干预方法翻译改进

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Counterfactual Causal-Effect Intervention for Interpretable Medical Visual Question Answering

一种用于可解释的医学视觉问答的反事实因果效应干预方法 翻译改进

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Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity

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Implementation of Bartlett matched-field processing using interpretable complex convolutional neural network

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