Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising.
Convolutional neural networks (CNN) have been used for a wide variety of deep learning applications, especially in computer vision. For medical image processing, researchers have identified certain challenges associated with CNNs. These challenges encompass the generation of less informative feature...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2669 - 2688 |
|---|---|
| Autores principales: | , , |
| Formato: | computer program diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515411&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515411 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515411 181515411 181515411 10.1007/s10278-024-01108-8 181515411 ppf: 2669 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising. aug: au: Marcos, Luella Babyn, Paul Alirezaie, Javad affil: https://ror.org/05g13zd79 Department of Electrical, Biomedical and Computer Engineering, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, M5B 2K3, Toronto, Ontario, Canada sug: subj: Tomography, X-Ray Computed Methods Radiation Dosage Neural Networks (Computer) Image Processing, Computer Assisted Deep Learning Prediction Models Human Quantitative Studies Machine Learning Algorithms Conceptual Framework Sensitivity and Specificity Visual Perception Diagnostic Imaging ab: Convolutional neural networks (CNN) have been used for a wide variety of deep learning applications, especially in computer vision. For medical image processing, researchers have identified certain challenges associated with CNNs. These challenges encompass the generation of less informative features, limitations in capturing both high and low-frequency information within feature maps, and the computational cost incurred when enhancing receptive fields by deepening the network. Transformers have emerged as an approach aiming to address and overcome these specific limitations of CNNs in the context of medical image analysis. Preservation of all spatial details of medical images is necessary to ensure accurate patient diagnosis. Hence, this research introduced the use of a pure Vision Transformer (ViT) for a denoising artificial neural network for medical image processing specifically for low-dose computed tomography (LDCT) image denoising. The proposed model follows a U-Net framework that contains ViT modules with the integration of Noise2Neighbor (N2N) interpolation operation. Five different datasets containing LDCT and normal-dose CT (NDCT) image pairs were used to carry out this experiment. To test the efficacy of the proposed model, this experiment includes comparisons between the quantitative and visual results among CNN-based (BM3D, RED-CNN, DRL-E-MP), hybrid CNN-ViT-based (TED-Net), and the proposed pure ViT-based denoising model. The findings of this study showed that there is about 15–20% increase in SSIM and PSNR when using self-attention transformers than using the typical pure CNN. Visual results also showed improvements especially when it comes to showing fine structural details of CT images. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|