Medical Image Denosing via Explainable AI Feature Preserving Loss
- URL: http://arxiv.org/abs/2310.20101v2
- Date: Tue, 7 Nov 2023 20:41:59 GMT
- Title: Medical Image Denosing via Explainable AI Feature Preserving Loss
- Authors: Guanfang Dong and Anup Basu
- Abstract summary: 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.
Our feature preserving loss function is motivated by the characteristic that gradient-based XAI is sensitive to noise.
- Score: 7.027732392103466
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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