基于可解释AI特征保持损失的医学图像去噪
Medical Image Denosing via Explainable AI Feature Preserving Loss
- University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对经典医学图像去噪算法忽略可解释性与关键医学特征保留的问题，提出基于梯度XAI的特征保持损失函数的去噪方法，在多数据集多噪声类型下验证了其去噪性能、可解释性与泛化性优势。
AI中文摘要：
去噪算法在医学图像处理与分析中发挥着关键作用。然而，经典去噪算法往往忽略了可解释性与关键医学特征的保留，这可能导致误诊与法律责任。在本研究中，我们提出了一种新的医学图像去噪方法，该方法不仅能有效去除各类噪声，还能在整个过程中保留关键医学特征。为实现这一目标，我们采用基于梯度的可解释人工智能（eXplainable Artificial Intelligence, XAI）方法设计了特征保持损失函数。我们的特征保持损失函数的设计灵感来源于基于梯度的XAI对噪声敏感这一特性。通过反向传播，可使去噪前后的医学图像特征保持一致。我们在三个公开医学图像数据集上开展了大量实验，涵盖合成的13种不同类型的噪声与伪影。实验结果表明，我们的方法在去噪性能、模型可解释性与泛化性方面均具有优越性。
英文摘要：
Denoising algorithms play a crucial role in medical image processing and analysis. However, classical denoising algorithms often ignore explanatory and critical medical features preservation, which may lead to misdiagnosis and legal liabilities. In this work, we propose a new denoising method for medical images that not only efficiently removes various types of noise, but also preserves key medical features throughout the process. To achieve this goal, we utilize a gradient-based eXplainable Artificial Intelligence (XAI) approach to design a feature preserving loss function. Our feature preserving loss function is motivated by the characteristic that gradient-based XAI is sensitive to noise. Through backpropagation, medical image features before and after denoising can be kept consistent. We conducted extensive experiments on three available medical image datasets, including synthesized 13 different types of noise and artifacts. The experimental results demonstrate the superiority of our method in terms of denoising performance, model explainability, and generalization.
