Abstract
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 this https URL.
Abstract (translated)
病理学图像分析是准确癌症诊断和治疗规划的关键。尽管深度学习模型(特别是卷积神经网络)在這個領域取得了進展,但它們的“黑盒子”特點引起了對可解釋性和可信度的關注。可解釋性人工智能(XAI)技術旨在解決這些問題,但評估其有效性仍然具有挑戰性。目前基于遮挡的XAI方法的一个主要問題是,它們通常會生成离群(OoD)樣本,導致不準確的評估。在本文中,我們介紹了基于修复的遮挡(IBO),一種新的遮挡策略,它利用去噪扩散概率模型的特性來在歷史學圖像中修復被遮罩的區域。通過用真實的非癌症組織替換癌細胞區域,IBO最小化OoD artifacts並保留了數據完整性。我們通過CAMELYON16數據集進行實驗,分為兩個階段進行評估:第一階段,使用學習到的感知相似性(LPIPS)指標評估感知相似性;第二階段,通過曲线下面積(AUC)分析評估模型的預測影響。我們的研究結果表明,IBO顯著提高了感知準確性,與最優秀的現有屏蔽策略相比,LPIPS得分進步了近兩倍。此外,IBO將XAI性能預測的準確度從42%提高到71%,與傳統方法相比。這些結果表明,IBO具有提供更多可靠性的XAI技術的潛力,有助於病理學和其他應用。本研究的研究源代碼可在這個https URL找到。
URL
https://arxiv.org/abs/2408.16395