  Deep learning had already demonstrated its power in medical images, including
denoising, classification, segmentation, etc. All these applications are
proposed to automatically analyze medical images beforehand, which brings more
information to radiologists during clinical assessment for accuracy
improvement. Recently, many medical denoising methods had shown their
significant artifact reduction result and noise removal both quantitatively and
qualitatively. However, those existing methods are developed around
human-vision, i.e., they are designed to minimize the noise effect that can be
perceived by human eyes. In this paper, we introduce an application-guided
denoising framework, which focuses on denoising for the following neural
networks. In our experiments, we apply the proposed framework to different
datasets, models, and use cases. Experimental results show that our proposed
framework can achieve a better result than human-vision denoising network.
