Masked Autoencoders for Low dose CT denoising Article Swipe
Dayang Wang
,
Yongshun Xu
,
Shuo Han
,
Hengyong Yu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.04944
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.04944
Low-dose computed tomography (LDCT) reduces the X-ray radiation but compromises image quality with more noises and artifacts. A plethora of transformer models have been developed recently to improve LDCT image quality. However, the success of a transformer model relies on a large amount of paired noisy and clean data, which is often unavailable in clinical applications. In computer vision and natural language processing fields, masked autoencoders (MAE) have been proposed as an effective label-free self-pretraining method for transformers, due to its excellent feature representation ability. Here, we redesign the classical encoder-decoder learning model to match the denoising task and apply it to LDCT denoising problem. The MAE can leverage the unlabeled data and facilitate structural preservation for the LDCT denoising model when ground truth data are missing. Experiments on the Mayo dataset validate that the MAE can boost the transformer's denoising performance and relieve the dependence on the ground truth data.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.04944
- https://arxiv.org/pdf/2210.04944
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4305006089
All OpenAlex metadata
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https://openalex.org/W4305006089Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.04944Digital Object Identifier
- Title
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Masked Autoencoders for Low dose CT denoisingWork title
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preprintOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-10-10Full publication date if available
- Authors
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Dayang Wang, Yongshun Xu, Shuo Han, Hengyong YuList of authors in order
- Landing page
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https://arxiv.org/abs/2210.04944Publisher landing page
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https://arxiv.org/pdf/2210.04944Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2210.04944Direct OA link when available
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Noise reduction, Computer science, Transformer, Artificial intelligence, Leverage (statistics), Ground truth, Encoder, Pattern recognition (psychology), Computer vision, Engineering, Voltage, Operating system, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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