Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment

Brown, J. M., Wilson, W.R. Exploiting tumour hypoxia in cancer treatment, Nature Reviews Cancer, vol.4(6), pp.437,(2004).

Article  CAS  PubMed  Google Scholar 

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).

Leppnen, 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‐like receptors 2, 4 and 9 and hypoxia markers HIF‐1alpha and CAIX in pancreatic intraepithelial neoplasia, Wiley Online Library, Apmis, vol.126(11), pp.852–863, (2018).

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).

Article  CAS  Google Scholar 

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).

CAS  Google Scholar 

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.1–10, (2019).

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).

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).

Article  Google Scholar 

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. 569–574, (2001).

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).

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).

Article  CAS  PubMed  Google Scholar 

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.172–177, (2008).

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.1097–1105,(2012).

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).

Article  Google Scholar 

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)

Bayramoglu, N., Heikkil, J. Transfer learning for cell nuclei classification in histopathology images, In: European Conference on Computer Vision, Springer, pp.532–539, (2016).

Qaiser, T., Mukherjee, A., Reddy Pb, C., Munugoti, S.D., Tallam, V., Pitkaho, T., Lehtimki, 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.227–238, (2018).

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)

Mukundan R: Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides. Journal of Imaging pp. 5–35, (2019).

Tewary S., Arun I., Ahmed R., Chatterjee S., Mukhopadhyay S., et al: AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers. Journal of Microscopy 281:pp. 87-96, (2021).

Article  CAS  PubMed  Google Scholar 

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. 297–300, (2012).

Pitkäaho, T., Lehtimäki, 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. 1–104, (2016).

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).

Article  PubMed  Google Scholar 

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).

Article  CAS  PubMed  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Drew, C.P., Shieh, W.-J. Immunohistochemistry, In: Current Laboratory Techniques in Rabies Diagnosis, Research and Prevention, Elsevier, vol.2, pp.109--115, (2015).

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)

CAS  PubMed  Google Scholar 

Mormont, R., Geurts, P., Mare, R. Comparison of deep transfer learning strategies for digital pathology, In Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.2262–2271, (2018).

Mikoajczyk, A., Grochowski, M. Data augmentation for improving deep learning in image classification problem, In 2018 international interdisciplinary PhD workshop (IIPhDW), pp. 117–122, (2018).

Shorten, C., Khoshgoftaar, T.M. A survey on image data augmentation for deep learning, Springer, J. Big Data, vol.6, pp. 60 (2019).

Article  Google Scholar 

Sokolova, M., Lapalme, G. A systematic analysis of performance measures for classification tasks, Elsevier Journal of Information Processing \& Management, vol.45(4), pp. 466–475, (2009).

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. 1–9, (2015).

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.3320–3328, (2014).

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. 1299–1312, (2016).

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. 188–196, (2016).

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).

Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition International Conference on Learning Representations (ICLR), 2015.

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.770–778, (2016).

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. 4700–4708 (2017).

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).

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).

Article  PubMed  Google Scholar 

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—585,( 2020).

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—77, (2017).

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).

Comments (0)

No login
gif