{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:56Z","timestamp":1772138096167,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T00:00:00Z","timestamp":1561075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Protein Research Unit Ruhr within Europe"},{"name":"Ministry of Innovation, Science and Research"},{"name":"MIWF"},{"name":"North-Rhine Westphalia","award":["233-1.08.03.03-031-68079"],"award-info":[{"award-number":["233-1.08.03.03-031-68079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which are non-linear in nature and lead to classifiers that do not generalize well, e.g. across different types of tissue preparation. Furthermore, existing preprocessing approaches involve iterative procedures that are computationally demanding, so that computation time required for preprocessing does not keep pace with recent progress in infrared microscopes which can capture whole-slide images within minutes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We investigate the application of stacked contractive autoencoders as an unsupervised approach to preprocess infrared microscopic pixel spectra, followed by supervised fine-tuning to obtain neural networks that can reliably resolve tissue structure. To validate the robustness of the resulting classifier, we demonstrate that a network trained on embedded tissue can be transferred to classify fresh frozen tissue. The features obtained from unsupervised pretraining thus generalize across the large spectral differences between embedded and fresh frozen tissue, where under previous approaches separate classifiers had to be trained from scratch.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Our implementation can be downloaded from https:\/\/github.com\/arnrau\/SCAE_IR_Spectral_Imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz505","type":"journal-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T15:13:48Z","timestamp":1560438828000},"page":"287-294","source":"Crossref","is-referenced-by-count":20,"title":["Deep representation learning for domain adaptable classification of infrared spectral imaging data"],"prefix":"10.1093","volume":"36","author":[{"given":"Arne P","family":"Raulf","sequence":"first","affiliation":[{"name":"Center for Protein Diagnostics (ProDi) , 44801 Bochum, Germany"},{"name":"Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universit\u00e4t Bochum , 44801 Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Butke","sequence":"additional","affiliation":[{"name":"Center for Protein Diagnostics (ProDi) , 44801 Bochum, Germany"},{"name":"Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universit\u00e4t Bochum , 44801 Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claus","family":"K\u00fcpper","sequence":"additional","affiliation":[{"name":"Center for Protein Diagnostics (ProDi) , 44801 Bochum, Germany"},{"name":"Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universit\u00e4t Bochum , 44801 Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frederik","family":"Gro\u00dferueschkamp","sequence":"additional","affiliation":[{"name":"Center for Protein Diagnostics (ProDi) , 44801 Bochum, Germany"},{"name":"Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universit\u00e4t Bochum , 44801 Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaus","family":"Gerwert","sequence":"additional","affiliation":[{"name":"Center for Protein Diagnostics (ProDi) , 44801 Bochum, Germany"},{"name":"Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universit\u00e4t Bochum , 44801 Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7266-8323","authenticated-orcid":false,"given":"Axel","family":"Mosig","sequence":"additional","affiliation":[{"name":"Center for Protein Diagnostics (ProDi) , 44801 Bochum, Germany"},{"name":"Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universit\u00e4t Bochum , 44801 Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"key":"2023013109500798300_btz505-B1","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1039\/b904808a","article-title":"Resonant Mie scattering in infrared spectroscopy of biological materials\u2013understanding the \u2018dispersion artefact\u2019","volume":"134","author":"Bassan","year":"2009","journal-title":"Analyst"},{"key":"2023013109500798300_btz505-B2","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1039\/B921056C","article-title":"Resonant Mie scattering (RMieS) correction of infrared spectra from highly scattering biological samples","volume":"135","author":"Bassan","year":"2010","journal-title":"Analyst"},{"key":"2023013109500798300_btz505-B3","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: a review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"2023013109500798300_btz505-B4","doi-asserted-by":"crossref","first-page":"1358.","DOI":"10.1038\/labinvest.2012.101","article-title":"Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer","volume":"92","author":"Bird","year":"2012","journal-title":"Lab. Investig"},{"key":"2023013109500798300_btz505-B5","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","article-title":"Big data deep learning: challenges and perspectives","volume":"2","author":"Chen","year":"2014","journal-title":"IEEE Access"},{"key":"2023013109500798300_btz505-B6","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1093\/bioinformatics\/btl355","article-title":"Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching","volume":"22","author":"Du","year":"2006","journal-title":"Bioinformatics"},{"key":"2023013109500798300_btz505-B7","first-page":"249","author":"Glorot","year":"2010"},{"key":"2023013109500798300_btz505-B8","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1039\/C4AN01978D","article-title":"Marker-free automated histopathological annotation of lung tumour subtypes by FTIR imaging","volume":"140","author":"Gro\u00dferueschkamp","year":"2015","journal-title":"Analyst"},{"key":"2023013109500798300_btz505-B9","doi-asserted-by":"crossref","first-page":"44829","DOI":"10.1038\/srep44829","article-title":"Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics","volume":"7","author":"Gro\u00dferueschkamp","year":"2017","journal-title":"Sci. Rep"},{"key":"2023013109500798300_btz505-B10","doi-asserted-by":"crossref","first-page":"4410","DOI":"10.1039\/C7AY01363A","article-title":"Common mistakes in cross-validating classification models","volume":"9","author":"Guo","year":"2017","journal-title":"Anal. Methods"},{"key":"2023013109500798300_btz505-B11","doi-asserted-by":"crossref","first-page":"9787","DOI":"10.1021\/acs.analchem.8b01536","article-title":"Extended multiplicative signal correction based model transfer for Raman spectroscopy in biological applications","volume":"90","author":"Guo","year":"2018","journal-title":"Anal. Chem"},{"key":"2023013109500798300_btz505-B12","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1002\/jbio.201200132","article-title":"Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections","volume":"6","author":"Kallenbach-Thieltges","year":"2013","journal-title":"J. Biophotonics"},{"key":"2023013109500798300_btz505-B13","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1366\/000370208783759669","article-title":"Estimating and correcting Mie scattering in synchrotron-based microscopic Fourier transform infrared spectra by extended multiplicative signal correction","volume":"62","author":"Kohler","year":"2008","journal-title":"Appl. Spectroscopy"},{"key":"2023013109500798300_btz505-B14","doi-asserted-by":"crossref","first-page":"e201600307.","DOI":"10.1002\/jbio.201600307","article-title":"An improved algorithm for fast resonant Mie scatter correction of infrared spectra of cells and tissues","volume":"11","author":"Konevskikh","year":"2018","journal-title":"J. Biophotonics"},{"key":"2023013109500798300_btz505-B15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1039\/C5FD00157A","article-title":"Label-free classification of colon cancer grading using infrared spectral histopathology","volume":"187","author":"Kuepper","year":"2016","journal-title":"Faraday Discuss"},{"key":"2023013109500798300_btz505-B16","doi-asserted-by":"crossref","first-page":"7717.","DOI":"10.1038\/s41598-018-26098-w","article-title":"Quantum cascade laser-based infrared microscopy for label-free and automated cancer classification in tissue sections","volume":"8","author":"Kuepper","year":"2018","journal-title":"Sci. Rep"},{"key":"2023013109500798300_btz505-B17","doi-asserted-by":"crossref","first-page":"2958","DOI":"10.1039\/c2an15868j","article-title":"Noise adjusted principal component reconstruction to optimize infrared microspectroscopy of individual live cells","volume":"137","author":"Marcsisin","year":"2012","journal-title":"Analyst"},{"key":"2023013109500798300_btz505-B18","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1039\/c2an35582e","article-title":"Line shape distortion effects in infrared spectroscopy","volume":"137","author":"Miljkovi\u0107","year":"2012","journal-title":"Analyst"},{"key":"2023013109500798300_btz505-B19","doi-asserted-by":"crossref","first-page":"3635","DOI":"10.1529\/biophysj.104.057950","article-title":"Mie-type scattering and non-beer-lambert absorption behavior of human cells in infrared microspectroscopy","volume":"88","author":"Mohlenhoff","year":"2005","journal-title":"Biophys. J"},{"key":"2023013109500798300_btz505-B20","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"2023013109500798300_btz505-B21","first-page":"833","author":"Rifai","year":"2011"},{"key":"2023013109500798300_btz505-B22","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.vibspec.2005.04.003","article-title":"Correction of dispersive line shape artifact observed in diffuse reflection infrared spectroscopy and absorption\/reflection (transflection) infrared micro-spectroscopy","volume":"38","author":"Romeo","year":"2005","journal-title":"Vibration. Spectroscopy"},{"key":"2023013109500798300_btz505-B23","author":"Simonyan","year":"2013"},{"key":"2023013109500798300_btz505-B24","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res"},{"key":"2023013109500798300_btz505-B25","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1145\/1390156.1390294","volume-title":"Proceedings of the 25th International Conference on Machine Learning","author":"Vincent","year":"2008"},{"key":"2023013109500798300_btz505-B26","first-page":"3371","article-title":"Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res"},{"key":"2023013109500798300_btz505-B27","doi-asserted-by":"crossref","first-page":"324.","DOI":"10.1186\/1471-2105-9-324","article-title":"Baseline correction for NMR spectroscopic metabolomics data analysis","volume":"9","author":"Xi","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023013109500798300_btz505-B28","doi-asserted-by":"crossref","first-page":"6893","DOI":"10.1021\/acs.analchem.7b01403","article-title":"Noninvasive diagnosis of high-grade urothelial carcinoma in urine by Raman spectral imaging","volume":"89","author":"Yosef","year":"2017","journal-title":"Anal. Chem"},{"key":"2023013109500798300_btz505-B29","doi-asserted-by":"crossref","first-page":"e1002683.","DOI":"10.1371\/journal.pmed.1002683","article-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study","volume":"15","author":"Zech","year":"2018","journal-title":"PLoS Med"},{"key":"2023013109500798300_btz505-B30","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1039\/b922045c","article-title":"Baseline correction using adaptive iteratively reweighted penalized least squares","volume":"135","author":"Zhang","year":"2010","journal-title":"Analyst"},{"key":"2023013109500798300_btz505-B31","first-page":"2921","author":"Zhou","year":"2016"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btz505\/28924864\/btz505.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/1\/287\/48981312\/bioinformatics_36_1_287.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/1\/287\/48981312\/bioinformatics_36_1_287.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T13:28:59Z","timestamp":1675171739000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/1\/287\/5521621"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2019,6,21]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz505","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/584227","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,1,1]]},"published":{"date-parts":[[2019,6,21]]}}}