References
[1] S. Turker-Kaya, C.W. Huck, A Review of Mid-Infrared and Near-Infrared Imaging: Principles, Concepts and Applications in Plant Tissue Analysis, Molecules 22 (2017).
[2] E. Kontsek, A. Pesti, M. Bjornstedt, T. Uveges, E. Szabo, T. Garay, P. Gordon, S. Gergely, A. Kiss, Mid-Infrared Imaging Is Able to Characterize and Separate Cancer Cell Lines, Pathol Oncol Res 26 (2020) 2401-2407.
[3] S. Mittal, R. Bhargava, A comparison of mid-infrared spectral regions on accuracy of tissue classification, Analyst 144 (2019) 2635-2642.
[4] J. Nallala, G.R. Lloyd, N. Shepherd, N. Stone, High-resolution FTIR imaging of colon tissues for elucidation of individual cellular and histopathological features, Analyst 141 (2016) 630-639.
[5] J.A. Kimber, L. Foreman, B. Turner, P. Rich, S.G. Kazarian, FTIR spectroscopic imaging and mapping with correcting lenses for studies of biological cells and tissues, Faraday Discuss 187 (2016) 69-85.
[6] L.S. Leslie, T.P. Wrobel, D. Mayerich, S. Bindra, R. Emmadi, R. Bhargava, High definition infrared spectroscopic imaging for lymph node histopathology, PLoS One 10 (2015) e0127238.
[7] H. Sreedhar, V.K. Varma, P.L. Nguyen, B. Davidson, S. Akkina, G. Guzman, S. Setty, A. Kajdacsy-Balla, M.J. Walsh, High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging of human tissue sections towards improving pathology, J Vis Exp (2015) 52332.
[8] C.H. Petter, N. Heigl, M. Rainer, R. Bakry, J. Pallua, G.K. Bonn, C.W. Huck, Development and application of Fourier-transform infrared chemical imaging of tumour in human tissue, Curr Med Chem 16 (2009) 318-326.
[9] J.D. Pallua, C. Pezzei, B. Zelger, G. Schaefer, L.K. Bittner, V.A. Huck-Pezzei, S.A. Schoenbichler, H. Hahn, A. Kloss-Brandstaetter, F. Kloss, G.K. Bonn, C.W. Huck, Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma, Analyst 137 (2012) 3965-3974.
[10] C. Pezzei, J.D. Pallua, G. Schaefer, C. Seifarth, V. Huck-Pezzei, L.K. Bittner, H. Klocker, G. Bartsch, G.K. Bonn, C.W. Huck, Characterization of normal and malignant prostate tissue by Fourier transform infrared microspectroscopy, Mol Biosyst 6 (2010) 2287-2295.
[11] J. Laimer, R. Henn, T. Helten, S. Sprung, B. Zelger, B. Zelger, R. Steiner, D. Schnabl, V. Offermanns, E. Bruckmoser, C.W. Huck, Amalgam tattoo versus melanocytic neoplasm - Differential diagnosis of dark pigmented oral mucosa lesions using infrared spectroscopy, PLoS One 13 (2018) e0207026.
[12] M. Isabelle, K. Rogers, N. Stone, Correlation mapping: rapid method for identification of histological features and pathological classification in mid infrared spectroscopic images of lymph nodes, J Biomed Opt 15 (2010) 026030.
[13] E. Willenbacher, A. Brunner, B. Zelger, S.H. Unterberger, R. Stalder, C.W. Huck, W. Willenbacher, J.D. Pallua, Application of mid-infrared microscopic imaging for the diagnosis and classification of human lymphomas, J Biophotonics 14 (2021) e202100079.
[14] M.J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H.J. Butler, K.M. Dorling, P.R. Fielden, S.W. Fogarty, N.J. Fullwood, K.A. Heys, C. Hughes, P. Lasch, P.L. Martin-Hirsch, B. Obinaju, G.D. Sockalingum, J. Sule-Suso, R.J. Strong, M.J. Walsh, B.R. Wood, P. Gardner, F.L. Martin, Using Fourier transform IR spectroscopy to analyze biological materials, Nat Protoc 9 (2014) 1771-1791.
[15] P. Lasch, W. Haensch, D. Naumann, M. Diem, Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis, Biochim Biophys Acta 1688 (2004) 176-186.
[16] D.C. Fernandez, R. Bhargava, S.M. Hewitt, I.W. Levin, Infrared spectroscopic imaging for histopathologic recognition, Nat Biotechnol 23 (2005) 469-474.
[17] R.S. Uysal, I.H. Boyaci, Authentication of liquid egg composition using ATR-FTIR and NIR spectroscopy in combination with PCA, J Sci Food Agric 100 (2020) 855-862.
[18] A. Beljebbar, S. Dukic, N. Amharref, M. Manfait, Screening of biochemical/histological changes associated to C6 glioma tumor development by FTIR/PCA imaging, Analyst 135 (2010) 1090-1097.
[19] G.C. Andrade, C.M. Medeiros Coelho, V.G. Uarrota, Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA), Heliyon 6 (2020) e03477.
[20] P. Barmpalexis, A. Karagianni, I. Nikolakakis, K. Kachrimanis, Artificial neural networks (ANNs) and partial least squares (PLS) regression in the quantitative analysis of cocrystal formulations by Raman and ATR-FTIR spectroscopy, J Pharm Biomed Anal 158 (2018) 214-224.
[21] Y. Kou, Q. Li, X. Liu, R. Zhang, X. Yu, Efficient Detection of Edible Oils Adulterated with Used Frying Oils through PE-film-based FTIR Spectroscopy Combined with DA and PLS, J Oleo Sci 67 (2018) 1083-1089.
[22] D. Kong, W. Peng, R. Zong, G. Cui, X. Yu, Morphological and Biochemical Properties of Human Astrocytes, Microglia, Glioma, and Glioblastoma Cells Using Fourier Transform Infrared Spectroscopy, Med Sci Monit 26 (2020) e925754.
[23] Y. Li, F. Li, X. Yang, L. Guo, F. Huang, Z. Chen, X. Chen, S. Zheng, Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM, Spectrochim Acta A Mol Biomol Spectrosc 201 (2018) 249-257.
[24] H.Z. Chen, G.Q. Tang, W. Ai, L.L. Xu, K. Cai, Use of random forest in FTIR analysis of LDL cholesterol and tri-glycerides for hyperlipidemia, Biotechnol Prog 31 (2015) 1693-1702.
[25] E. Kaznowska, J. Depciuch, K. Lach, M. Kolodziej, A. Koziorowska, J. Vongsvivut, I. Zawlik, M. Cholewa, J. Cebulski, The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model, Talanta 186 (2018) 337-345.
[26] P. Lasch, M. Stammler, M. Zhang, M. Baranska, A. Bosch, K. Majzner, FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Identification of Pathogenic Bacteria, Anal Chem 90 (2018) 8896-8904.
[27] S. Kimeswenger, P. Tschandl, P. Noack, M. Hofmarcher, E. Rumetshofer, H. Kindermann, R. Silye, S. Hochreiter, M. Kaltenbrunner, E. Guenova, G. Klambauer, W. Hoetzenecker, Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns, Mod Pathol 34 (2021) 895-903.
[28] H.R. Tizhoosh, L. Pantanowitz, Artificial Intelligence and Digital Pathology: Challenges and Opportunities, J Pathol Inform 9 (2018) 38.
[29] S.J. MacEachern, N.D. Forkert, Machine learning for precision medicine, Genome 64 (2021) 416-425.
[30] I.H. Sarker, Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions, SN Comput Sci 2 (2021) 420.
[31] S. Park, H.M. Gach, S. Kim, S.J. Lee, Y. Motai, Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI, IEEE J Transl Eng Health Med 9 (2021) 1800113.
[32] J.H. Jang, T.Y. Kim, H.S. Lim, D. Yoon, Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder, PLoS One 16 (2021) e0260612.
[33] H. Takahashi, A. Emami, T. Shinozaki, N. Kunii, T. Matsuo, K. Kawai, Convolutional neural network with autoencoder-assisted multiclass labelling for seizure detection based on scalp electroencephalography, Comput Biol Med 125 (2020) 104016.
[34] M. Roy, J. Kong, S. Kashyap, V.P. Pastore, F. Wang, K.C.L. Wong, V. Mukherjee, Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images, Sci Rep 11 (2021) 139.
[35] V. Rastogi, N. Puri, S. Arora, G. Kaur, L. Yadav, R. Sharma, Artefacts: a diagnostic dilemma - a review, J Clin Diagn Res 7 (2013) 2408-2413.
[36] S. Chatterjee, Artefacts in histopathology, J Oral Maxillofac Pathol 18 (2014) S111-116.
[37] N. Grabe, W. Roth, S. Foersch, [Digital pathology in immuno-oncology-current opportunities and challenges : Overview of the analysis of immune cell infiltrates using whole slide imaging], Pathologe 39 (2018) 539-545.
[38] G. Andrade, R. Ferreira, G. Teodoro, L. Rocha, J.H. Saltz, T. Kurc, Efficient Execution of Microscopy Image Analysis on CPU, GPU, and MIC Equipped Cluster Systems, Proc Symp Comput Archit High Perform Comput 2014 (2014) 89-96.