Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention.
Automatic tissue segmentation in whole-slide images (WSIs) is a critical task in hematoxylin and eosin- (H&E-) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segment...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/7/2022
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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=157865096&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157865096 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/7/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157865096 157865096 157865096 10.1155/2022/7966553 157865096 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention. aug: au: Xu, Rui Wang, Zhizhen Liu, Zhenbing Han, Chu Yan, Lixu Lin, Huan Xu, Zeyan Feng, Zhengyun Liang, Changhong Chen, Xin Pan, Xipeng Liu, Zaiyi affil: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China sug: subj: Lung Neoplasms Diagnosis Risk Assessment Neural Networks (Computer) Lung Neoplasms Pathology Human Algorithms Colorectal Neoplasms Software ab: Automatic tissue segmentation in whole-slide images (WSIs) is a critical task in hematoxylin and eosin- (H&E-) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segmentation of WSIs. However, due to the tumour heterogeneity, large intraclass variability and small interclass variability make the classification task challenging. In this paper, we propose a novel bilinear convolutional neural network- (Bilinear-CNN-) based model with a bilinear convolutional module and a soft attention module to tackle this problem. This method investigates the intraclass semantic correspondence and focuses on the more distinguishable features that make feature output variations relatively large between interclass. The performance of the Bilinear-CNN-based model is compared with other state-of-the-art methods on the histopathological classification dataset, which consists of 107.7 k patches of lung cancer. We further evaluate our proposed algorithm on an additional dataset from colorectal cancer. Extensive experiments show that the performance of our proposed method is superior to that of previous state-of-the-art ones and the interpretability of our proposed method is demonstrated by Grad-CAM. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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