Medical Image Denosing via Explainable AI Feature Preserving Loss
This addresses the risk of misdiagnosis and legal liabilities in medical imaging by improving feature preservation, though it appears incremental as it builds on existing denoising and XAI techniques.
The paper tackles the problem of medical image denoising by proposing a method that removes noise while preserving key medical features, using a gradient-based XAI approach to design a feature-preserving loss function, and demonstrates superiority in denoising performance, explainability, and generalization on three datasets with 13 noise types.
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.