mlsanityData to 7 Oct 2026

Task-Oriented Low-Dose CT Image Denoising

arXiv 2103.13557 ↗First posted 25 Mar 2021eess.IV cs.CVcited by 11

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Abstract (arXiv)

The extensive use of medical CT has raised a public concern over the radiation dose to the patient. Reducing the radiation dose leads to increased CT image noise and artifacts, which can adversely affect not only the radiologists judgement but also the performance of downstream medical image analysis tasks. Various low-dose CT denoising methods, especially the recent deep learning based approaches, have produced impressive results. However, the existing denoising methods are all downstream-task-agnostic and neglect the diverse needs of the downstream applications. In this paper, we introduce a novel Task-Oriented Denoising Network (TOD-Net) with a task-oriented loss leveraging knowledge from the downstream tasks. Comprehensive empirical analysis shows that the task-oriented loss complements other task agnostic losses by steering the denoiser to enhance the image quality in the task related regions of interest. Such enhancement in turn brings general boosts on the performance of various methods for the downstream task. The presented work may shed light on the future development of context-aware image denoising methods.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

6 of 20 references.

References · 6

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  3. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  4. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 260
  5. 2019The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomescited by 168
  6. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 3; 2026: 1.

11 papers.

  1. 20233DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representationcited by 24
  2. 2022When Neural Networks Fail to Generalize? A Model Sensitivity Perspectivecited by 10
  3. 2021Impact of deep learning-based image super-resolution on binary signal detectioncited by 9
  4. 2024Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluationcited by 9
  5. 2023Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptationcited by 8
  6. 2022R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classificationcited by 2
  7. 2025Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learningcited by 2
  8. 2024Revisiting Spurious Correlation in Domain Generalizationcited by 1
  9. 2025PPORLD-EDNetLDCT: A Proximal Policy Optimization-Based Reinforcement Learning Framework for Adaptive Low-Dose CT Denoisingcited by 0
  10. 2025Task-Adaptive Low-Dose CT Reconstructioncited by 0
  11. 2026Image-Domain Poisson-Perturbation Robustness of NCCT Slice Classificationcited by 0