Medical Image Denosing via Explainable AI Feature Preserving Loss

Abstract: 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.