  Deep learning has achieved notable performance in the denoising task of
low-quality medical images and the detection task of lesions, respectively.
However, existing low-quality medical image denoising approaches are
disconnected from the detection task of lesions. Intuitively, the quality of
denoised images will influence the lesion detection accuracy that in turn can
be used to affect the denoising performance. To this end, we propose a
play-and-plug medical image denoising framework, namely Lesion-Inspired
Denoising Network (LIDnet), to collaboratively improve both denoising
performance and detection accuracy of denoised medical images. Specifically, we
propose to insert the feedback of downstream detection task into existing
denoising framework by jointly learning a multi-loss objective. Instead of
using perceptual loss calculated on the entire feature map, a novel
region-of-interest (ROI) perceptual loss induced by the lesion detection task
is proposed to further connect these two tasks. To achieve better optimization
for overall framework, we propose a customized collaborative training strategy
for LIDnet. On consideration of clinical usability and imaging characteristics,
three low-dose CT images datasets are used to evaluate the effectiveness of the
proposed LIDnet. Experiments show that, by equipping with LIDnet, both of the
denoising and lesion detection performance of baseline methods can be
significantly improved.
