TACE：肿瘤感知的反事实解释
TACE: Tumor-Aware Counterfactual Explanations
AI总结：
提出TACE框架，通过仅修改肿瘤区域生成可靠反事实解释，在乳腺X光和脑MRI上显著提升分类成功率，超越现有方法。
摘要：
深度学习在医学影像中的应用显著提升了诊断能力，增强了准确性和效率。尽管有这些好处，这些AI模型缺乏透明度，常被称为“黑箱”，这引发了对其在临床环境中可靠性的担忧。可解释人工智能（XAI）旨在通过开发使AI决策可理解和可信的方法来缓解这些担忧。在本研究中，我们提出了肿瘤感知反事实解释（TACE），这是一个为医学图像生成可靠反事实解释的框架。与现有方法不同，TACE专注于修改肿瘤特定特征而不改变整体器官结构，确保反事实的忠实性。我们通过在生成过程中增加一个额外步骤来实现这一点，该步骤允许仅修改感兴趣区域（ROI），从而产生更可靠的反事实，因为器官的其余部分保持不变。我们在乳腺X光图像和脑部MRI上评估了我们的方法。我们发现，我们的方法在反事实的质量、忠实性和生成速度方面远超现有最先进技术。确实，更忠实的解释导致分类成功率的显著提升，乳腺癌提高了10.69%，脑肿瘤提高了98.02%。我们工作的代码可在https://github.com/ispamm/TACE获取。
英文摘要：
The application of deep learning in medical imaging has significantly advanced diagnostic capabilities, enhancing both accuracy and efficiency. Despite these benefits, the lack of transparency in these AI models, often termed "black boxes," raises concerns about their reliability in clinical settings. Explainable AI (XAI) aims to mitigate these concerns by developing methods that make AI decisions understandable and trustworthy. In this study, we propose Tumor Aware Counterfactual Explanations (TACE), a framework designed to generate reliable counterfactual explanations for medical images. Unlike existing methods, TACE focuses on modifying tumor-specific features without altering the overall organ structure, ensuring the faithfulness of the counterfactuals. We achieve this by including an additional step in the generation process which allows to modify only the region of interest (ROI), thus yielding more reliable counterfactuals as the rest of the organ remains unchanged. We evaluate our method on mammography images and brain MRI. We find that our method far exceeds existing state-of-the-art techniques in quality, faithfulness, and generation speed of counterfactuals. Indeed, more faithful explanations lead to a significant improvement in classification success rates, with a 10.69% increase for breast cancer and a 98.02% increase for brain tumors. The code of our work is available at https://github.com/ispamm/TACE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
