SAA-SDM: Neural Networks Faster Learned to Segment Organ Images.
In the field of medicine, rapidly and accurately segmenting organs in medical images is a crucial application of computer technology. This paper introduces a feature map module, Strength Attention Area Signed Distance Map (SAA-SDM), based on the principal component analysis (PCA) principle. The modu...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 547 - 563 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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| 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=177625997&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177625997 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177625997 177625997 177625997 10.1007/s10278-023-00947-1 177625997 ppf: 547 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: SAA-SDM: Neural Networks Faster Learned to Segment Organ Images. aug: au: Gao, Chao Shi, Yongtao Yang, Shuai Lei, Bangjun affil: https://ror.org/0419nfc77 College of Computer and Information Technology, China Three Gorges University, 443002, Yichang Hubei, China sug: subj: Neural Networks (Computer) Diagnostic Imaging Methods Decision Making Human Factor Analysis Semantic Analysis Tomography, X-Ray Computed Descriptive Statistics Learning Methods ab: In the field of medicine, rapidly and accurately segmenting organs in medical images is a crucial application of computer technology. This paper introduces a feature map module, Strength Attention Area Signed Distance Map (SAA-SDM), based on the principal component analysis (PCA) principle. The module is designed to accelerate neural networks' convergence speed in rapidly achieving high precision. SAA-SDM provides the neural network with confidence information regarding the target and background, similar to the signed distance map (SDM), thereby enhancing the network's understanding of semantic information related to the target. Furthermore, this paper presents a training scheme tailored for the module, aiming to achieve finer segmentation and improved generalization performance. Validation of our approach is carried out using TRUS and chest X-ray datasets. Experimental results demonstrate that our method significantly enhances neural networks' convergence speed and precision. For instance, the convergence speed of UNet and UNET + + is improved by more than 30%. Moreover, Segformer achieves an increase of over 6% and 3% in mIoU (mean Intersection over Union) on two test datasets without requiring pre-trained parameters. Our approach reduces the time and resource costs associated with training neural networks for organ segmentation tasks while effectively guiding the network to achieve meaningful learning even without pre-trained parameters. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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