{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T00:13:12Z","timestamp":1744503192304,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract\n<\/jats:title><jats:p>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 <jats:italic>Feat-Concat<\/jats:italic> 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.<\/jats:p>","DOI":"10.1007\/s10278-023-00859-0","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T17:01:42Z","timestamp":1693933302000},"page":"2367-2381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1333-9268","authenticated-orcid":false,"given":"Maisun Mohamed","family":"Al Zorgani","sequence":"first","affiliation":[]},{"given":"Hassan","family":"Ugail","sequence":"additional","affiliation":[]},{"given":"Klaus","family":"Pors","sequence":"additional","affiliation":[]},{"given":"Abdullahi Magaji","family":"Dauda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"issue":"6","key":"859_CR1","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1038\/nrc1367","volume":"4","author":"JM Brown","year":"2004","unstructured":"Brown, J. M., Wilson, W.R. Exploiting tumour hypoxia in cancer treatment, Nature Reviews Cancer, vol.4(6), pp.437,(2004).","journal-title":"Nature Reviews Cancer"},{"key":"859_CR2","doi-asserted-by":"crossref","unstructured":"Mirabello, V., Cortezon-Tamarit, F. and Pascu, S.I., 2018. Oxygen sensing, hypoxia tracing and in vivo imaging with functional metalloprobes for the early detection of non-communicable diseases, Frontiers in chemistry, vol.6, p.27, (2018).","DOI":"10.3389\/fchem.2018.00027"},{"key":"859_CR3","doi-asserted-by":"crossref","unstructured":"Lepp{\\\"a}nen, J., Helminen, O., Huhta, H., Kauppila, J.H., Isohookana, J., Haapasaari, K.M., Karihtala, P., Parkkila, S., Saarnio, J., Lehenkari, P.P. and Karttunen, T.J., 2018. Toll\u2010like receptors 2, 4 and 9 and hypoxia markers HIF\u20101alpha and CAIX in pancreatic intraepithelial neoplasia, Wiley Online Library, Apmis, vol.126(11), pp.852\u2013863, (2018).","DOI":"10.1111\/apm.12894"},{"issue":"2","key":"859_CR4","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1038\/s41588-018-0318-2","volume":"51","author":"V Bhandari","year":"2019","unstructured":"Bhandari, V., Hoey, C., Liu, L.Y., Lalonde, E., Ray, J., Livingstone, J., Lesurf, R., Shiah, Y.J., Vujcic, T., Huang, X. and Espiritu, S.M. Molecular landmarks of tumor hypoxia across cancer types, Nature Publishing Group, Nature genetics, vol.51(2), pp.308-318, (2019).","journal-title":"Nature Publishing Group, Nature genetics"},{"issue":"1","key":"859_CR5","first-page":"1","volume":"10","author":"I Godet","year":"2019","unstructured":"Godet, I., Shin, Y.J., Ju, J.A., Ye, I.C., Wang, G. and Gilkes, D.M. Fate-mapping post-hypoxic tumor cells reveals a ROS-resistant phenotype that promotes metastasis, Nature communications, Nature Publishing Group, Nature communications, vol.10(1), pp.1-18, (2019).","journal-title":"Nature communications, Nature Publishing Group, Nature communications"},{"key":"859_CR6","doi-asserted-by":"crossref","unstructured":"Zhao, S., Yu, W., Ukon, N., Tan, C., Nishijima, K.I., Shimizu, Y., Higashikawa, K., Shiga, T., Yamashita, H., Tamaki, N. and Kuge, Y., 2019. Elimination of tumor hypoxia by eribulin demonstrated by 18 F-FMISO hypoxia imaging in human tumor xenograft models, Springer, EJNMMI research, vol.9(1), pp.1\u201310, (2019).","DOI":"10.1186\/s13550-019-0521-x"},{"key":"859_CR7","doi-asserted-by":"crossref","unstructured":"Meier, V., Guscetti, F., Roos, M., Ohlerth, S., Pruschy, M. and Rohrer Bley, C. Hypoxia-related marker GLUT-1, CAIX, proliferative index and microvessel density in canine oral malignant neoplasia, Public Library of Science San Francisco, CA USA, PloS one, 11(2), p.e0149993, (2016).","DOI":"10.1371\/journal.pone.0149993"},{"issue":"2","key":"859_CR8","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1021\/acs.molpharmaceut.8b00950","volume":"16","author":"FJ Huizing","year":"2018","unstructured":"Huizing, F.J., Hoeben, B.A., Franssen, G.M., Boerman, O.C., Heskamp, S. and Bussink, J. Quantitative imaging of the hypoxia-related marker CAIX in head and neck squamous cell carcinoma xenograft models. ACS Publications, Molecular pharmaceutics, 16(2), pp.701-708, (2018).","journal-title":"ACS Publications, Molecular pharmaceutics"},{"key":"859_CR9","doi-asserted-by":"crossref","unstructured":"Raleigh, J.A., Chou, S-C., Bono, E.L., Thrall, D.E., Varia, M.A. Semiquantitative immunohistochemical analysis for hypoxia in human tumors, Elsevier International Journal of Radiation Oncology* Biology* Physics, vol.49(2), pp. 569\u2013574, (2001).","DOI":"10.1016\/S0360-3016(00)01505-4"},{"key":"859_CR10","unstructured":"Manu, V., Hein, T.A., Boruah, D., Srinivas, V. Serous ovarian tumors: Immunohistochemical profiling as an aid to grading and understanding tumorigenesis, Medical Journal Armed Forces India,(2018)."},{"issue":"4","key":"859_CR11","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1158\/1078-0432.CCR-07-4020","volume":"14","author":"MR Albertella","year":"2008","unstructured":"Albertella, M.R., Loadman, P.M., Jones, P.H., Phillips, R.M., Rampling, R., et al. Hypoxia-selective targeting by the bioreductive prodrug AQ4N in patients with solid tumors: results of a phase I study, Clinical cancer research,vol.14(4), pp.1096-1104, (2008).","journal-title":"Clinical cancer research"},{"key":"859_CR12","doi-asserted-by":"crossref","unstructured":"Sullivan, C.AW., Chung, G.G. Biomarker validation: in situ analysis of protein expression using semiquantitative immunohistochemistry-based techniques, Clinical colorectal cancer, vol.7(3), pp.172\u2013177, (2008).","DOI":"10.3816\/CCC.2008.n.022"},{"key":"859_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks, In Proc. 25th International Conference on Neural Information Processing Systems, NIPS'12 Current Associates Inc., USA, pp.1097\u20131105,(2012)."},{"key":"859_CR14","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., et al. A survey on deep learning in medical image analysis, Elsevier journal of medical image analysis, vol.42, pp. 60-88, (2017).","journal-title":"Elsevier journal of medical image analysis"},{"key":"859_CR15","doi-asserted-by":"crossref","unstructured":"Stathonikos, N., Veta, M., Huisman, A., van Diest, P.J. Going fully digital: Perspective of a Dutch academic pathology lab, J. of pathol. inform., vol.4(1), pp.15, (2013)","DOI":"10.4103\/2153-3539.114206"},{"key":"859_CR16","doi-asserted-by":"crossref","unstructured":"Bayramoglu, N., Heikkil{\\\"a}, J. Transfer learning for cell nuclei classification in histopathology images, In: European Conference on Computer Vision, Springer, pp.532\u2013539, (2016).","DOI":"10.1007\/978-3-319-49409-8_46"},{"key":"859_CR17","doi-asserted-by":"crossref","unstructured":"Qaiser, T., Mukherjee, A., Reddy Pb, C., Munugoti, S.D., Tallam, V., Pitk{\\\"a}aho, T., Lehtim{\\\"a}ki, T., et al. Her 2 challenge contest: a detailed assessment of automated her 2 scoring algorithms in whole slide images of breast cancer tissues, Histopathology, vol.72(2), pp.227\u2013238, (2018).","DOI":"10.1111\/his.13333"},{"key":"859_CR18","doi-asserted-by":"crossref","unstructured":"Cordeiro C.Q., Ioshii S.O., Alves J.H., Oliveira L.F. et al: An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features. arXiv preprint, (2018)","DOI":"10.5753\/sbcas.2018.3685"},{"key":"859_CR19","doi-asserted-by":"crossref","unstructured":"Mukundan R: Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides. Journal of Imaging pp. 5\u201335, (2019).","DOI":"10.3390\/jimaging5030035"},{"key":"859_CR20","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1111\/jmi.12955","volume":"281","author":"S Tewary","year":"2021","unstructured":"Tewary S., Arun I., Ahmed R., Chatterjee S., Mukhopadhyay S., et al: AutoIHC\u2010Analyzer: computer\u2010assisted microscopy for automated membrane extraction\/scoring in HER2 molecular markers. Journal of Microscopy 281:pp. 87-96, (2021).","journal-title":"Journal of Microscopy"},{"key":"859_CR21","doi-asserted-by":"crossref","unstructured":"Chang, C.-Y., Huang Y.-C., Ko C.-C. Automatic analysis of her-2\/neu immunohistochemistry in breast cancer, in: 2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications, IEEE, pp. 297\u2013300, (2012).","DOI":"10.1109\/IBICA.2012.72"},{"key":"859_CR22","unstructured":"Pitk\u00e4aho, T., Lehtim\u00e4ki, T.M., McDonald, J. and Naughton, T.J.: Classifying HER2 breast cancer cell samples using deep learning. In Proc. Irish Mach. Vis. Image Process. Conf, pp. 1\u2013104, (2016)."},{"issue":"5","key":"859_CR23","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1109\/TIP.2018.2795742","volume":"27","author":"M Saha","year":"2018","unstructured":"Saha, M. and Chakraborty, C.: Her2Net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation. IEEE Transactions on Image Processing, 27(5), pp.2189-2200,(2018).","journal-title":"IEEE Transactions on Image Processing"},{"key":"859_CR24","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.compbiomed.2019.05.020","volume":"110","author":"FD Khameneh","year":"2019","unstructured":"Khameneh, F.D., Razavi, S. and Kamasak, M., Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network. Computers in biology and medicine, 110, pp.164-174, (2019).","journal-title":"Computers in biology and medicine"},{"key":"859_CR25","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/s10278-021-00442-5","volume":"34","author":"S Tewary","year":"2021","unstructured":"Tewary, S. and Mukhopadhyay, S., HER2 molecular marker scoring using transfer learning and decision level fusion. Journal of Digital Imaging, 34, pp.667-677,(2021).","journal-title":"Journal of Digital Imaging"},{"key":"859_CR26","doi-asserted-by":"crossref","unstructured":"Drew, C.P., Shieh, W.-J. Immunohistochemistry, In: Current Laboratory Techniques in Rabies Diagnosis, Research and Prevention, Elsevier, vol.2, pp.109--115, (2015).","DOI":"10.1016\/B978-0-12-801919-1.00010-5"},{"issue":"4","key":"859_CR27","first-page":"291","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok, A.C., Johnston, D.A., et al. Quantification of histochemical staining by color deconvolution, Analytical and quantitative cytology and histology, vol.23(4), pp. 291--299, (2001)","journal-title":"Analytical and quantitative cytology and histology"},{"key":"859_CR28","doi-asserted-by":"crossref","unstructured":"Mormont, R., Geurts, P., Mar{\\'e}e, R. Comparison of deep transfer learning strategies for digital pathology, In Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.2262\u20132271, (2018).","DOI":"10.1109\/CVPRW.2018.00303"},{"key":"859_CR29","doi-asserted-by":"crossref","unstructured":"Miko{\\l}ajczyk, A., Grochowski, M. Data augmentation for improving deep learning in image classification problem, In 2018 international interdisciplinary PhD workshop (IIPhDW), pp. 117\u2013122, (2018).","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"859_CR30","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M. A survey on image data augmentation for deep learning, Springer, J. Big Data, vol.6, pp. 60 (2019).","journal-title":"J. Big Data"},{"key":"859_CR31","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Lapalme, G. A systematic analysis of performance measures for classification tasks, Elsevier Journal of Information Processing \\& Management, vol.45(4), pp. 466\u2013475, (2009).","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"859_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. et al. Going deeper with convolutions, In: Proc. IEEE conference on computer vision and pattern recognition, pp. 1\u20139, (2015).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"859_CR33","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H., How transferable are features in deep neural networks?, In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol.2, pp.3320\u20133328, (2014)."},{"key":"859_CR34","doi-asserted-by":"crossref","unstructured":"Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning?, journal of IEEE transactions on medical imaging, vol.35(5), pp. 1299\u20131312, (2016).","DOI":"10.1109\/TMI.2016.2535302"},{"key":"859_CR35","doi-asserted-by":"crossref","unstructured":"Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam, S., Annangi, P. Understanding the mechanisms of deep transfer learning for medical images, In: Deep Learning and Data Labeling for Medical Applications, Springer, pp. 188\u2013196, (2016).","DOI":"10.1007\/978-3-319-46976-8_20"},{"key":"859_CR36","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L. Imagenet: A large-scale hierarchical image database, In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248--255,(2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"859_CR37","unstructured":"Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition International Conference on Learning Representations (ICLR), 2015."},{"key":"859_CR38","doi-asserted-by":"crossref","unstructured":"He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, J. Deep residual learning for image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition, PP.770\u2013778, (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"859_CR39","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Densely connected convolutional networks, In Proc.of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"859_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J. Shufflenet: An extremely efficient convolutional neural network for mobile devices, In Proce. of the IEEE conference on computer vision and pattern recognition, pp. 6848--6856 (2018).","DOI":"10.1109\/CVPR.2018.00716"},{"issue":"1","key":"859_CR41","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/TPAMI.2008.266","volume":"32","author":"S Escalera","year":"2010","unstructured":"Escalera, S., Pujol, O., Radeva, P.On the Decoding Process in Ternary Error-Correcting Output Codes, IEEE transactions on pattern analysis and machine intelligence, vol.32(1), pp. 120-134, (2010).","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"859_CR42","doi-asserted-by":"crossref","unstructured":"Jammal, M., Canu, S.,Abdallah, M. R., Sparse Support Vector Machines via Mixed Integer Programming, In International Conference on Machine Learning, Optimization, and Data Science, Springer, pp. 572\u2014585,( 2020).","DOI":"10.1007\/978-3-030-64580-9_47"},{"key":"859_CR43","doi-asserted-by":"crossref","unstructured":"Yao, L., Zeng, F., Li, D.-H., Chen, Z.-G. Sparse Support Vector Machine with L p Penalty for Feature Selection, Journal of Computer Science and Technology, Springer, vol(1)32,pp. 68\u201477, (2017).","DOI":"10.1007\/s11390-017-1706-2"},{"key":"859_CR44","unstructured":"Kahya, M. A., Al-Hayani, W., Algamal, Z. Y. Classification of breast cancer histopathology images based on adaptive sparse support vector machine, Journal of Applied Mathematics and Bioinformatics, vol.(1)7, pp.49,(2017)."}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00859-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00859-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00859-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T21:52:25Z","timestamp":1702936345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00859-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,5]]},"references-count":44,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["859"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00859-0","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"type":"print","value":"0897-1889"},{"type":"electronic","value":"1618-727X"}],"subject":[],"published":{"date-parts":[[2023,9,5]]},"assertion":[{"value":"21 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}