Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment.
Glucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the GLUT-1 scoring is a routine procedure currently employed for predicting tumour hypoxia markers in clinical practice. However, visual assessment of GLUT-1...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 6; pp. 2367 - 2382 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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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=173050931&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173050931 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2023 vid: 36 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173050931 171383250 173050931 173050931 10.1007/s10278-023-00859-0 173050931 ppf: 2367 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment. aug: au: Al Zorgani, Maisun Mohamed Ugail, Hassan Pors, Klaus Dauda, Abdullahi Magaji affil: https://ror.org/00vs8d940 Faculty of Engineering and Informatics, School of Media, Design and Technology, University of Bradford, Richmond Road, BD7 1DP, Bradford, UK sug: subj: Learning Methods Automation Deep Learning Methods Carrier Proteins Metabolism Immunohistochemistry Methods Gene Expression Colorectal Neoplasms Diagnosis Tumor Markers, Biological Human Algorithms Neural Networks (Computer) Descriptive Statistics Comparative Studies Support Vector Machine Image Processing, Computer Assisted Cell Physiology ab: Glucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the GLUT-1 scoring is a routine procedure currently employed for predicting tumour hypoxia markers in clinical practice. However, visual assessment of GLUT-1 scores is subjective and consequently prone to inter-pathologist variability. Therefore, this study proposes an automated method for assessing GLUT-1 scores in IHC colorectal carcinoma images. For this purpose, we leverage deep transfer learning methodologies for evaluating the performance of six different pre-trained convolutional neural network (CNN) architectures: AlexNet, VGG16, GoogleNet, ResNet50, DenseNet-201 and ShuffleNet. The target CNNs are fine-tuned as classifiers or adapted as feature extractors with support vector machine (SVM) to classify GLUT-1 scores in IHC images. Our experimental results show that the winning model is the trained SVM classifier on the extracted deep features fusion Feat-Concat from DenseNet201, ResNet50 and GoogLeNet extractors. It yields the highest prediction accuracy of 98.86%, thus outperforming the other classifiers on our dataset. We also conclude, from comparing the methodologies, that the off-the-shelf feature extraction is better than the fine-tuning model in terms of time and resources required for training. 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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