mlsanityData to 7 Oct 2026

TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising

arXiv 2106.04650 ↗First posted 8 Jun 2021eess.IV cs.CV physics.med-phcited by 21

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Abstract (arXiv)

Low dose computed tomography is a mainstream for clinical applications. How-ever, compared to normal dose CT, in the low dose CT (LDCT) images, there are stronger noise and more artifacts which are obstacles for practical applications. In the last few years, convolution-based end-to-end deep learning methods have been widely used for LDCT image denoising. Recently, transformer has shown superior performance over convolution with more feature interactions. Yet its ap-plications in LDCT denoising have not been fully cultivated. Here, we propose a convolution-free T2T vision transformer-based Encoder-decoder Dilation net-work (TED-net) to enrich the family of LDCT denoising algorithms. The model is free of convolution blocks and consists of a symmetric encoder-decoder block with sole transformer. Our model is evaluated on the AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance over the state-of-the-art denoising methods.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

20 of 35 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 12; 2023: 5; 2024: 2; 2025: 1.

21 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  3. 2022CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoisingcited by 46
  4. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  5. 2022Class-Aware Adversarial Transformers for Medical Image Segmentationcited by 33
  6. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  7. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
  8. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  9. 2023Transformers in Healthcare: A Surveycited by 17
  10. 2022A Comprehensive Survey of Transformers for Computer Visioncited by 13
  11. 2023LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurringcited by 12
  12. 2024DenoMamba: A fused state-space model for low-dose CT denoisingcited by 11
9 more
  1. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  2. 2022Vision Transformers in Medical Imaging: A Reviewcited by 8
  3. 2024Transformers-based architectures for stroke segmentation: A reviewcited by 5
  4. 2023Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Modelcited by 3
  5. 2022Manifoldron: Direct Space Partition via Manifold Discoverycited by 2
  6. 20223D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volumecited by 2
  7. 2023Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomographycited by 1
  8. 2025A-IDE : Agent-Integrated Denoising Expertscited by 1
  9. 2021TransMorph: Transformer for unsupervised medical image registrationcited by 0