DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs.
Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer (CRC) assists pathologists in better cancer grading and prognostication. In thi...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1653 - 1663 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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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=169808793&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808793 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808793 163069070 169808793 169808793 10.1007/s10278-023-00797-x 169808793 ppf: 1653 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs. aug: au: Baidar Bakht, Ahsan Javed, Sajid Gilani, Syed Qasim Karki, Hamad Muneeb, Muhammad Werghi, Naoufel affil: Electrical and Computer Engineering Department, Khalifa University, 12778, Abu Dhabi, United Arab Emirates sug: subj: Colorectal Neoplasms Pathology Tissue Analysis Phenotype Deep Learning Algorithms Cell Physiology Histology Methods Human Funding Source Pathologists Lymphocytes Muscle, Smooth Models, Statistical Descriptive Statistics Pathology, Molecular Methods Automation, Laboratory Neoplasm Grading Colorectal Neoplasms Prognosis Colorectal Neoplasms Diagnosis ab: Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer (CRC) assists pathologists in better cancer grading and prognostication. In this paper, we propose a novel algorithm for the identification of distinct tissue components in colon cancer histology images by blending a comprehensive learning system with deep features extraction in the current work. Firstly, we extracted the features from the pre-trained VGG19 network which are then transformed into mapped features space for nodes enhancement generation. Utilizing both mapped features and enhancement nodes, the proposed algorithm classifies seven distinct tissue components including stroma, tumor, complex stroma, necrotic, normal benign, lymphocytes, and smooth muscle. To validate our proposed model, the experiments are performed on two publicly available colorectal cancer histology datasets. We showcase that our approach achieves a remarkable performance boost surpassing existing state-of-the-art methods by (1.3% AvTP, 2% F1) and (7% AvTP, 6% F1) on CRCD-1, and CRCD-2, respectively. 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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