IBO：基于修复的遮挡方法以增强组织病理学中可解释人工智能评估
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology
AI总结：
本文提出基于修复的遮挡方法IBO，利用去噪扩散概率模型修复组织病理学图像中的遮挡区域，减少分布外样本，并在CAMELYON16数据集上将XAI性能预测精确度从42%提升至71%。
摘要：
组织病理学图像分析对于准确的癌症诊断和治疗规划至关重要。尽管深度学习模型，尤其是卷积神经网络，已经推动了该领域的发展，但其“黑箱”特性引发了人们对可解释性和可信度的担忧。可解释人工智能（XAI）技术旨在解决这些担忧，但评估其有效性仍然具有挑战性。当前基于遮挡的XAI方法存在的一个重大问题是，它们通常会产生分布外（OoD）样本，从而导致评估不准确。在本文中，我们提出了基于修复的遮挡（IBO），这是一种新颖的遮挡策略，利用去噪扩散概率模型来修复组织病理学图像中被遮挡的区域。通过用逼真的非癌变组织替换癌变区域，IBO最大限度地减少了OoD伪影并保持了数据完整性。我们在CAMELYON16数据集上通过两个阶段评估了我们的方法：首先，使用学习感知图像块相似度（LPIPS）指标评估感知相似性；其次，通过曲线下面积（AUC）分析量化对模型预测的影响。我们的结果表明，IBO显著提高了感知保真度，与现有最佳遮挡策略相比，LPIPS分数的提升接近两倍。此外，与传统方法相比，IBO将XAI性能预测的精确度从42%提高到71%。这些结果表明，IBO有潜力为XAI技术提供更可靠的评估，从而使组织病理学和其他应用受益。本研究的源代码可在https://github.com/a-fsh-r/IBO获取。
英文摘要：
Histopathological image analysis is crucial for accurate cancer diagnosis and treatment planning. While deep learning models, especially convolutional neural networks, have advanced this field, their "black-box" nature raises concerns about interpretability and trustworthiness. Explainable Artificial Intelligence (XAI) techniques aim to address these concerns, but evaluating their effectiveness remains challenging. A significant issue with current occlusion-based XAI methods is that they often generate Out-of-Distribution (OoD) samples, leading to inaccurate evaluations. In this paper, we introduce Inpainting-Based Occlusion (IBO), a novel occlusion strategy that utilizes a Denoising Diffusion Probabilistic Model to inpaint occluded regions in histopathological images. By replacing cancerous areas with realistic, non-cancerous tissue, IBO minimizes OoD artifacts and preserves data integrity. We evaluate our method on the CAMELYON16 dataset through two phases: first, by assessing perceptual similarity using the Learned Perceptual Image Patch Similarity (LPIPS) metric, and second, by quantifying the impact on model predictions through Area Under the Curve (AUC) analysis. Our results demonstrate that IBO significantly improves perceptual fidelity, achieving nearly twice the improvement in LPIPS scores compared to the best existing occlusion strategy. Additionally, IBO increased the precision of XAI performance prediction from 42% to 71% compared to traditional methods. These results demonstrate IBO's potential to provide more reliable evaluations of XAI techniques, benefiting histopathology and other applications. The source code for this study is available at https://github.com/a-fsh-r/IBO.
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。
