# Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection

arXiv: https://arxiv.org/abs/2104.08845 · HTML: https://mlsanity.com/paper/2104.08845/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?](https://mlsanity.com/paper/1811.03691/) (2018)
- [3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network](https://mlsanity.com/paper/1802.05656/) (2018)
- [Connecting Image Denoising and High-Level Vision Tasks via Deep Learning](https://mlsanity.com/paper/1809.01826/) (2018)
- [DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification](https://mlsanity.com/paper/1801.09555/) (2018)
- [Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising](https://mlsanity.com/paper/1805.00587/) (2018)
- [Deep Learning for Low-Dose CT Denoising](https://mlsanity.com/paper/1902.10127/) (2019)
- [An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images](https://mlsanity.com/paper/1706.03008/) (2017)

## Built on by (most cited first, 7 of 7)

- [Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior](https://mlsanity.com/paper/2504.11286/) (2025)
- [Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment](https://mlsanity.com/paper/2302.13251/) (2023)
- [OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions](https://mlsanity.com/paper/2504.13524/) (2025)
- [A Complementary Global and Local Knowledge Network for Ultrasound denoising with Fine-grained Refinement](https://mlsanity.com/paper/2310.03402/) (2023)
- [TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation](https://mlsanity.com/paper/2502.09931/) (2025)
- [DTEA: Dynamic Topology Weaving and Instability-Driven Entropic Attenuation for Medical Image Segmentation](https://mlsanity.com/paper/2510.11259/) (2025)
- [Prior-guided Hierarchical Instance-pixel Contrastive Learning for Ultrasound Speckle Noise Suppression](https://mlsanity.com/paper/2602.13831/) (2026)

## Abstract (arXiv)

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.