{"entity": "researcher", "timestamp": "2026-07-18T03:15:40.743Z", "family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "affiliations": ["Core Facility Bioimaging at the Biomedical Center and Walter-Brendel-Zentrum f\u00fcr Experimentelle Medizin, Ludwig-Maximilians-Univerist\u00e4t M\u00fcnchen,Gro\u00dfhaderner Stra\u00dfe 9, 82152 Planegg-Martinsried,Germany."], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13"}}, "publications": [{"entity": "publication", "iuid": "bb241ed48fe94700bafa1c93cc2bba33", "links": {"self": {"href": "https://publications.scilifelab.se/publication/bb241ed48fe94700bafa1c93cc2bba33.json"}, "display": {"href": "https://publications.scilifelab.se/publication/bb241ed48fe94700bafa1c93cc2bba33"}}, "title": "The crucial role of bioimage analysts in scientific research and publication.", "authors": [{"family": "Cimini", "given": "Beth A", "initials": "BA", "orcid": "0000-0001-9640-9318", "researcher": {"href": "https://publications.scilifelab.se/researcher/db23220ace18412a872eeedc98b6ca3d.json"}}, {"family": "Bankhead", "given": "Peter", "initials": "P", "orcid": "0000-0003-4851-8813", "researcher": {"href": "https://publications.scilifelab.se/researcher/677800a098f9480aad3629a35a6d7145.json"}}, {"family": "D'Antuono", "given": "Rocco", "initials": "R", "orcid": "0000-0003-0180-6500", "researcher": {"href": "https://publications.scilifelab.se/researcher/19a219cdf18a48c581c66844eed7f888.json"}}, {"family": "Fazeli", "given": "Elnaz", "initials": "E", "orcid": "0000-0002-0770-0777", "researcher": {"href": "https://publications.scilifelab.se/researcher/18bdf85441724603af3433b9ca2671a7.json"}}, {"family": "Fernandez-Rodriguez", "given": "Julia", "initials": "J", "orcid": "0000-0003-4522-0966", "researcher": {"href": "https://publications.scilifelab.se/researcher/3bd9c743e99d479cb752aeec947acab3.json"}}, {"family": "Fuster-Barcel\u00f3", "given": "Caterina", "initials": "C", "orcid": "0000-0002-4784-6957", "researcher": {"href": "https://publications.scilifelab.se/researcher/d79b3864cd2746e2b17df5513a86d75c.json"}}, {"family": "Haase", "given": "Robert", "initials": "R", "orcid": "0000-0001-5949-2327", "researcher": {"href": "https://publications.scilifelab.se/researcher/f99dc020f05f479cafa0aede600157f0.json"}}, {"family": "Jambor", "given": "Helena Klara", "initials": "HK", "orcid": "0000-0003-3397-1842", "researcher": {"href": "https://publications.scilifelab.se/researcher/4d9c995274164d5298f59dbd5c088ab7.json"}}, {"family": "Jones", "given": "Martin L", "initials": "ML", "orcid": "0000-0003-0994-5652", "researcher": {"href": "https://publications.scilifelab.se/researcher/b0007265969c41f48df95f3c74897453.json"}}, {"family": "Jug", "given": "Florian", "initials": "F", "orcid": "0000-0002-8499-5812", "researcher": {"href": "https://publications.scilifelab.se/researcher/40ccce2be15f4d8fab6a75615ebc1246.json"}}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Kreshuk", "given": "Anna", "initials": "A", "orcid": "0000-0003-1334-6388", "researcher": {"href": "https://publications.scilifelab.se/researcher/0ae251ba20e04f1eab188633a94e49bd.json"}}, {"family": "Marcotti", "given": "Stefania", "initials": "S", "orcid": "0000-0002-2877-0133", "researcher": {"href": "https://publications.scilifelab.se/researcher/150857cd632243249afeba74751e0c22.json"}}, {"family": "Martins", "given": "Gabriel G", "initials": "GG", "orcid": "0000-0002-6506-9776", "researcher": {"href": "https://publications.scilifelab.se/researcher/c04a08e246854401b529158afe9de318.json"}}, {"family": "McArdle", "given": "Sara", "initials": "S", "orcid": "0000-0003-3795-3772", "researcher": {"href": "https://publications.scilifelab.se/researcher/93aa1ff33b784bd0bfa75b471cdb54df.json"}}, {"family": "Miura", "given": "Kota", "initials": "K", "orcid": "0000-0001-6926-191X", "researcher": {"href": "https://publications.scilifelab.se/researcher/d2924dc08df743e188b5ead9a88449a6.json"}}, {"family": "Mu\u00f1oz-Barrutia", "given": "Arrate", "initials": "A", "orcid": "0000-0002-1573-1661", "researcher": {"href": "https://publications.scilifelab.se/researcher/55b6136b104f46ad958ffe4aa41b9714.json"}}, {"family": "Murphy", "given": "Laura C", "initials": "LC", "orcid": "0000-0003-0029-0434", "researcher": {"href": "https://publications.scilifelab.se/researcher/5bf84771f58b47839a2d1a39961c83f3.json"}}, {"family": "Nelson", "given": "Michael S", "initials": "MS", "orcid": "0000-0003-0480-5597", "researcher": {"href": "https://publications.scilifelab.se/researcher/3897b6add2a340e19ad1e051f08931c5.json"}}, {"family": "N\u00f8rrelykke", "given": "Simon F", "initials": "SF", "orcid": "0000-0001-8302-526X", "researcher": {"href": "https://publications.scilifelab.se/researcher/76912657154446a988bbcbdd146b9877.json"}}, {"family": "Paul-Gilloteaux", "given": "Perrine", "initials": "P", "orcid": "0000-0002-4822-165X", "researcher": {"href": "https://publications.scilifelab.se/researcher/cc1aedddd2484c60b1e6bcf3aff9262a.json"}}, {"family": "Pengo", "given": "Thomas", "initials": "T", "orcid": "0000-0002-9632-918X", "researcher": {"href": "https://publications.scilifelab.se/researcher/150939b6bb54471fa96434ef52fe8d7e.json"}}, {"family": "Pylv\u00e4n\u00e4inen", "given": "Joanna W", "initials": "JW", "orcid": "0000-0002-3540-5150", "researcher": {"href": "https://publications.scilifelab.se/researcher/b86cf81244ee420b9f00a654709bbfda.json"}}, {"family": "Pytowski", "given": "Lior", "initials": "L", "orcid": "0000-0002-8530-5123", "researcher": {"href": "https://publications.scilifelab.se/researcher/c808c56b5e0a431dae719c6e4feb70e1.json"}}, {"family": "Ravera", "given": "Arianna", "initials": "A", "orcid": "0009-0006-6585-261X", "researcher": {"href": "https://publications.scilifelab.se/researcher/2873293a0e174e0599374a7ba9516787.json"}}, {"family": "Reinke", "given": "Annika", "initials": "A", "orcid": "0000-0003-4363-1876", "researcher": {"href": "https://publications.scilifelab.se/researcher/ac4e522151164f09aac3b83068537ea1.json"}}, {"family": "Rekik", "given": "Yousr", "initials": "Y", "orcid": "0009-0001-1405-7257", "researcher": {"href": "https://publications.scilifelab.se/researcher/d8284d7240fa4f57ade366fd602623e0.json"}}, {"family": "Strambio-De-Castillia", "given": "Caterina", "initials": "C", "orcid": "0000-0002-1069-1816", "researcher": {"href": "https://publications.scilifelab.se/researcher/d56f91373ceb40bab6bae2635961bdf5.json"}}, {"family": "Th\u00e9di\u00e9", "given": "Daniel", "initials": "D", "orcid": "0000-0002-1352-7245", "researcher": {"href": "https://publications.scilifelab.se/researcher/b504b532ff944ab9ab72329adc6282fd.json"}}, {"family": "Uhlmann", "given": "Virginie", "initials": "V", "orcid": "0000-0002-2859-9241", "researcher": {"href": "https://publications.scilifelab.se/researcher/c4215607e1ed4f18b2809de6c5e34df6.json"}}, {"family": "Umney", "given": "Oliver", "initials": "O", "orcid": "0009-0005-2321-9413", "researcher": {"href": "https://publications.scilifelab.se/researcher/2ed29415dcf34d69b863d06e8edaa256.json"}}, {"family": "Wiggins", "given": "Laura", "initials": "L", "orcid": "0000-0003-4615-2379", "researcher": {"href": "https://publications.scilifelab.se/researcher/dbdc5616fe8542638669cbd647c46bb9.json"}}, {"family": "Eliceiri", "given": "Kevin W", "initials": "KW", "orcid": "0000-0001-8678-670X", "researcher": {"href": "https://publications.scilifelab.se/researcher/acb896fcca6a49b096068c3b5294624d.json"}}], "type": "journal article", "published": "2024-10-15", "journal": {"title": "J. Cell. Sci.", "issn": "1477-9137", "volume": "137", "issue": "20", "issn-l": "0021-9533"}, "abstract": "Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective - the result of the recent The Company of Biologists Workshop 'Effectively Communicating Bioimage Analysis', which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions - proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions. We also advocate for increased funding, standardized practices and enhanced collaboration, and we conclude with a call to action for all stakeholders to join efforts in advancing BIA.", "doi": "10.1242/jcs.262322", "pmid": "39475207", "labels": {"BioImage Informatics": "Collaborative", "Integrated Microscopy Technologies Gothenburg": "Service", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "362545"}], "notes": [], "created": "2024-11-13T10:08:24.614Z", "modified": "2024-11-15T12:02:35.328Z"}, {"entity": "publication", "iuid": "77c2cf62014a4e0390043fe01929db7f", "links": {"self": {"href": "https://publications.scilifelab.se/publication/77c2cf62014a4e0390043fe01929db7f.json"}, "display": {"href": "https://publications.scilifelab.se/publication/77c2cf62014a4e0390043fe01929db7f"}}, "title": "Automated detection of vascular remodeling in tumor-draining lymph nodes by the deep-learning tool HEV-finder.", "authors": [{"family": "Bekkhus", "given": "Tove", "initials": "T", "orcid": "0000-0002-2868-2483", "researcher": {"href": "https://publications.scilifelab.se/researcher/b28423917e67447bbb13a50006ed9e4e.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "Hanna", "given": "Sabella", "initials": "S"}, {"family": "Franz\u00e9n Boger", "given": "Mathias", "initials": "M"}, {"family": "Klemm", "given": "Anna", "initials": "A", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Bacovia", "given": "Daniel Vasiliu", "initials": "DV"}, {"family": "W\u00e4rnberg", "given": "Fredrik", "initials": "F", "orcid": "0000-0002-0130-7296", "researcher": {"href": "https://publications.scilifelab.se/researcher/25bbcad19da944faa9aa6b32377ae212.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Ulvmar", "given": "Maria H", "initials": "MH", "orcid": "0000-0002-9050-0978", "researcher": {"href": "https://publications.scilifelab.se/researcher/c7a0253b26d64189a014719cf1e4ab64.json"}}], "type": "journal article", "published": "2022-09-00", "journal": {"title": "J. Pathol.", "issn": "1096-9896", "issn-l": "0022-3417", "volume": "258", "issue": "1", "pages": "4-11"}, "abstract": "Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker. \u00a9 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.", "doi": "10.1002/path.5981", "pmid": "35696253", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pmc", "key": "PMC9543492"}], "notes": [], "created": "2022-08-04T08:33:08.492Z", "modified": "2022-11-02T06:20:15.622Z"}, {"entity": "publication", "iuid": "058e4ab4248b4f38bf463af3cb986da6", "links": {"self": {"href": "https://publications.scilifelab.se/publication/058e4ab4248b4f38bf463af3cb986da6.json"}, "display": {"href": "https://publications.scilifelab.se/publication/058e4ab4248b4f38bf463af3cb986da6"}}, "title": "A method for Boolean analysis of protein interactions at a molecular level.", "authors": [{"family": "Raykova", "given": "Doroteya", "initials": "D", "orcid": "0000-0001-6452-2199", "researcher": {"href": "https://publications.scilifelab.se/researcher/0a81c40491e349178167f148f2351875.json"}}, {"family": "Kermpatsou", "given": "Despoina", "initials": "D", "orcid": "0000-0001-5872-4472", "researcher": {"href": "https://publications.scilifelab.se/researcher/cfafe1fe67374c25ac5111ecd42c2fd4.json"}}, {"family": "Malmqvist", "given": "Tony", "initials": "T", "orcid": "0000-0003-0609-2009", "researcher": {"href": "https://publications.scilifelab.se/researcher/ee835b51354e4cb5aff5dfbf43964dc8.json"}}, {"family": "Harrison", "given": "Philip J", "initials": "PJ"}, {"family": "Sander", "given": "Marie Rubin", "initials": "MR", "orcid": "0000-0002-9783-5682", "researcher": {"href": "https://publications.scilifelab.se/researcher/68a774625f6948d693bf4ad09eaba273.json"}}, {"family": "Stiller", "given": "Christiane", "initials": "C", "orcid": "0000-0002-6552-8426", "researcher": {"href": "https://publications.scilifelab.se/researcher/2f8bb525e1094fbfbfb71dbbff62cbc7.json"}}, {"family": "Heldin", "given": "Johan", "initials": "J", "orcid": "0000-0002-0915-5303", "researcher": {"href": "https://publications.scilifelab.se/researcher/d8a546798d014cd3a44537ae5db9f889.json"}}, {"family": "Leino", "given": "Mattias", "initials": "M"}, {"family": "Ricardo", "given": "Sara", "initials": "S", "orcid": "0000-0003-4091-2226", "researcher": {"href": "https://publications.scilifelab.se/researcher/d5599dd22d9e44a5af16850baf14e10e.json"}}, {"family": "Klemm", "given": "Anna", "initials": "A", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "David", "given": "Leonor", "initials": "L", "orcid": "0000-0003-4207-9258", "researcher": {"href": "https://publications.scilifelab.se/researcher/510030f8783044e8b644277783308bf6.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications.scilifelab.se/researcher/605dbd52684d4e54ae4150a9933abe6e.json"}}, {"family": "Vemuri", "given": "Kalyani", "initials": "K", "orcid": "0000-0003-2544-5412", "researcher": {"href": "https://publications.scilifelab.se/researcher/b74c149757644aa5a0997af9ca583a45.json"}}, {"family": "Dimberg", "given": "Anna", "initials": "A", "orcid": "0000-0003-4422-9125", "researcher": {"href": "https://publications.scilifelab.se/researcher/c53166298a214331866c8cbf3bb9a3b9.json"}}, {"family": "Sundqvist", "given": "Anders", "initials": "A"}, {"family": "Norlin", "given": "Maria", "initials": "M", "orcid": "0000-0003-4348-6269", "researcher": {"href": "https://publications.scilifelab.se/researcher/a10ffe5af28b4398b8d19743f7927e34.json"}}, {"family": "Klaesson", "given": "Axel", "initials": "A"}, {"family": "Kampf", "given": "Caroline", "initials": "C"}, {"family": "S\u00f6derberg", "given": "Ola", "initials": "O", "orcid": "0000-0003-2883-1925", "researcher": {"href": "https://publications.scilifelab.se/researcher/68df823efa304c0b9962684ac1515808.json"}}], "type": "journal article", "published": "2022-08-13", "journal": {"title": "Nat Commun", "issn": "2041-1723", "volume": "13", "issue": "1", "pages": "4755", "issn-l": "2041-1723"}, "abstract": "Determining the levels of protein-protein interactions is essential for the analysis of signaling within the cell, characterization of mutation effects, protein function and activation in health and disease, among others. Herein, we describe MolBoolean - a method to detect interactions between endogenous proteins in various subcellular compartments, utilizing antibody-DNA conjugates for identification and signal amplification. In contrast to proximity ligation assays, MolBoolean simultaneously indicates the relative abundances of protein A and B not interacting with each other, as well as the pool of A and B proteins that are proximal enough to be considered an AB complex. MolBoolean is applicable both in fixed cells and tissue sections. The specific and quantifiable data that the method generates provide opportunities for both diagnostic use and medical research.", "doi": "10.1038/s41467-022-32395-w", "pmid": "35963857", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "10.1038/s41467-022-32395-w"}, {"db": "pmc", "key": "PMC9375095"}], "notes": [], "created": "2022-08-30T09:04:56.787Z", "modified": "2022-08-30T09:04:57.234Z"}, {"entity": "publication", "iuid": "4560332b88e042dcbe980dbee646b32d", "links": {"self": {"href": "https://publications.scilifelab.se/publication/4560332b88e042dcbe980dbee646b32d.json"}, "display": {"href": "https://publications.scilifelab.se/publication/4560332b88e042dcbe980dbee646b32d"}}, "title": "A Hitchhiker's guide through the bio-image analysis software universe.", "authors": [{"family": "Haase", "given": "Robert", "initials": "R", "orcid": "0000-0001-5949-2327", "researcher": {"href": "https://publications.scilifelab.se/researcher/f99dc020f05f479cafa0aede600157f0.json"}}, {"family": "Fazeli", "given": "Elnaz", "initials": "E", "orcid": "0000-0002-0770-0777", "researcher": {"href": "https://publications.scilifelab.se/researcher/18bdf85441724603af3433b9ca2671a7.json"}}, {"family": "Legland", "given": "David", "initials": "D", "orcid": "0000-0001-7456-4632", "researcher": {"href": "https://publications.scilifelab.se/researcher/432a4b146ab74e04a51ea04781fc2191.json"}}, {"family": "Doube", "given": "Michael", "initials": "M", "orcid": "0000-0002-8021-8127", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ef5d0dd88304b56b1434985f8793efc.json"}}, {"family": "Culley", "given": "Si\u00e2n", "initials": "S", "orcid": "0000-0003-2112-0143", "researcher": {"href": "https://publications.scilifelab.se/researcher/03e0652fe0bc4c3cb1da3df123ce5a31.json"}}, {"family": "Belevich", "given": "Ilya", "initials": "I", "orcid": "0000-0003-2190-4909", "researcher": {"href": "https://publications.scilifelab.se/researcher/1d3b7acb51a842d288640f31c89c0df8.json"}}, {"family": "Jokitalo", "given": "Eija", "initials": "E", "orcid": "0000-0002-4159-6934", "researcher": {"href": "https://publications.scilifelab.se/researcher/cd0afa4d27d545e59bbb3bd88b67e793.json"}}, {"family": "Schorb", "given": "Martin", "initials": "M", "orcid": "0000-0003-4910-1868", "researcher": {"href": "https://publications.scilifelab.se/researcher/50f9a26d2d094261a87ca3521b7cacfe.json"}}, {"family": "Klemm", "given": "Anna", "initials": "A", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Tischer", "given": "Christian", "initials": "C", "orcid": "0000-0003-4105-1990", "researcher": {"href": "https://publications.scilifelab.se/researcher/3dc57b68dd4e40d28414fec28783bcc4.json"}}], "type": "journal article", "published": "2022-07-14", "journal": {"title": "FEBS Lett.", "issn": "1873-3468", "issn-l": "0014-5793", "volume": null, "issue": null, "pages": null}, "abstract": "Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualising information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.", "doi": "10.1002/1873-3468.14451", "pmid": "35833863", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2022-08-04T08:27:41.790Z", "modified": "2022-08-04T08:29:03.431Z"}, {"entity": "publication", "iuid": "c20fafa5b31244a9ab60fc632f64d9c3", "links": {"self": {"href": "https://publications.scilifelab.se/publication/c20fafa5b31244a9ab60fc632f64d9c3.json"}, "display": {"href": "https://publications.scilifelab.se/publication/c20fafa5b31244a9ab60fc632f64d9c3"}}, "title": "Machine learning for cell classification and neighborhood analysis in glioma tissue.", "authors": [{"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications.scilifelab.se/researcher/24c62ca6579b4055a95d0dba53722939.json"}}, {"family": "Wik", "given": "Lina", "initials": "L", "orcid": "0000-0001-8406-1828", "researcher": {"href": "https://publications.scilifelab.se/researcher/ae592fc4e7404511a39f79638003a7aa.json"}}, {"family": "Olsson Bontell", "given": "Thomas", "initials": "T"}, {"family": "Wang", "given": "Yuyu", "initials": "Y"}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "\u00d6fverstedt", "given": "Johan", "initials": "J", "orcid": "0000-0003-0253-9037", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ade42e706714d9b8f0b9e7b8b26890d.json"}}, {"family": "Jakola", "given": "Asgeir S", "initials": "AS", "orcid": "0000-0002-2860-9331", "researcher": {"href": "https://publications.scilifelab.se/researcher/f2ebf72073a64dad9f5b16b38754d905.json"}}, {"family": "\u00d6stman", "given": "Arne", "initials": "A", "orcid": "0000-0003-3993-0021", "researcher": {"href": "https://publications.scilifelab.se/researcher/fa382835e9554f5db45fa6cd86f04eab.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2021-06-04", "journal": {"title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922"}, "abstract": "Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.", "doi": "10.1002/cyto.a.24467", "pmid": "34089228", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2021-11-29T12:36:20.429Z", "modified": "2021-11-29T12:36:20.618Z"}, {"entity": "publication", "iuid": "de4d7e55211c439e95a4878e3ab8842e", "links": {"self": {"href": "https://publications.scilifelab.se/publication/de4d7e55211c439e95a4878e3ab8842e.json"}, "display": {"href": "https://publications.scilifelab.se/publication/de4d7e55211c439e95a4878e3ab8842e"}}, "title": "Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists.", "authors": [{"family": "Martins", "given": "Gabriel G", "initials": "GG", "orcid": "0000-0002-6506-9776", "researcher": {"href": "https://publications.scilifelab.se/researcher/c04a08e246854401b529158afe9de318.json"}}, {"family": "Cordeli\u00e8res", "given": "Fabrice P", "initials": "FP", "orcid": "0000-0002-5383-5816", "researcher": {"href": "https://publications.scilifelab.se/researcher/cd3da472e85b4b19897bc513e443219f.json"}}, {"family": "Colombelli", "given": "Julien", "initials": "J", "orcid": "0000-0002-2784-4276", "researcher": {"href": "https://publications.scilifelab.se/researcher/e64e92116688467197a2eaadb170eb12.json"}}, {"family": "D'Antuono", "given": "Rocco", "initials": "R", "orcid": "0000-0003-0180-6500", "researcher": {"href": "https://publications.scilifelab.se/researcher/19a219cdf18a48c581c66844eed7f888.json"}}, {"family": "Golani", "given": "Ofra", "initials": "O", "orcid": "0000-0002-9793-236X", "researcher": {"href": "https://publications.scilifelab.se/researcher/e239b26ad266407eb249be51f728f2ef.json"}}, {"family": "Guiet", "given": "Romain", "initials": "R", "orcid": "0000-0001-6715-4897", "researcher": {"href": "https://publications.scilifelab.se/researcher/4e318b0b510843c194d0f1c6b852bceb.json"}}, {"family": "Haase", "given": "Robert", "initials": "R", "orcid": "0000-0001-5949-2327", "researcher": {"href": "https://publications.scilifelab.se/researcher/f99dc020f05f479cafa0aede600157f0.json"}}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Louveaux", "given": "Marion", "initials": "M", "orcid": "0000-0002-1794-3748", "researcher": {"href": "https://publications.scilifelab.se/researcher/3901ec2c7f1345f6974d7eae9766a502.json"}}, {"family": "Paul-Gilloteaux", "given": "Perrine", "initials": "P", "orcid": "0000-0002-4822-165X", "researcher": {"href": "https://publications.scilifelab.se/researcher/cc1aedddd2484c60b1e6bcf3aff9262a.json"}}, {"family": "Tinevez", "given": "Jean-Yves", "initials": "JY", "orcid": "0000-0002-0998-4718", "researcher": {"href": "https://publications.scilifelab.se/researcher/ed604822457048af8051bcb4e11da3a6.json"}}, {"family": "Miura", "given": "Kota", "initials": "K", "orcid": "0000-0001-6926-191X", "researcher": {"href": "https://publications.scilifelab.se/researcher/d2924dc08df743e188b5ead9a88449a6.json"}}], "type": "journal article", "published": "2021-04-30", "journal": {"title": "F1000Res", "issn": "2046-1402", "volume": "10", "pages": "334", "issn-l": "2046-1402"}, "abstract": "NEUBIAS, the European Network of Bioimage Analysts, was created in 2016 with the goal of improving the communication and the knowledge transfer among the various stakeholders involved in the acquisition, processing and analysis of biological image data, and to promote the establishment and recognition of the profession of Bioimage Analyst. One of the most successful initiatives of the NEUBIAS programme was its series of 15 training schools, which trained over 400 new Bioimage Analysts, coming from over 40 countries. Here we outline the rationale behind the innovative three-level program of the schools, the curriculum, the trainer recruitment and turnover strategy, the outcomes for the community and the career path of analysts, including some success stories. We discuss the future of the materials created during this programme and some of the new initiatives emanating from the community of NEUBIAS-trained analysts, such as the NEUBIAS Academy. Overall, we elaborate on how this training programme played a key role in collectively leveraging Bioimaging and Life Science research by bringing the latest innovations into structured, frequent and intensive training activities, and on why we believe this should become a model to further develop in Life Sciences.", "doi": "10.12688/f1000research.25485.1", "pmid": "34164115", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC8215561"}], "notes": [], "created": "2021-11-29T12:35:36.781Z", "modified": "2021-11-29T12:35:37.127Z"}, {"entity": "publication", "iuid": "859de35c946e4369a5530fc3194f10d1", "links": {"self": {"href": "https://publications.scilifelab.se/publication/859de35c946e4369a5530fc3194f10d1.json"}, "display": {"href": "https://publications.scilifelab.se/publication/859de35c946e4369a5530fc3194f10d1"}}, "title": "Bioimage analysis workflows: community resources to navigate through a complex ecosystem.", "authors": [{"family": "Paul-Gilloteaux", "given": "Perrine", "initials": "P", "orcid": "0000-0002-4822-165X", "researcher": {"href": "https://publications.scilifelab.se/researcher/cc1aedddd2484c60b1e6bcf3aff9262a.json"}}, {"family": "Tosi", "given": "S\u00e9bastien", "initials": "S", "orcid": "0000-0001-8348-2778", "researcher": {"href": "https://publications.scilifelab.se/researcher/94b7debd554a44b590a5c4bd2b802c82.json"}}, {"family": "H\u00e9rich\u00e9", "given": "Jean-Karim", "initials": "JK"}, {"family": "Gaignard", "given": "Alban", "initials": "A"}, {"family": "M\u00e9nager", "given": "Herv\u00e9", "initials": "H"}, {"family": "Mar\u00e9e", "given": "Rapha\u00ebl", "initials": "R"}, {"family": "Baecker", "given": "Volker", "initials": "V", "orcid": "0000-0002-9129-6403", "researcher": {"href": "https://publications.scilifelab.se/researcher/f5884e3569e5470f8d0a1fe960a85c1c.json"}}, {"family": "Klemm", "given": "Anna", "initials": "A", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Kala\u0161", "given": "Mat\u00fa\u0161", "initials": "M", "orcid": "0000-0002-1509-4981", "researcher": {"href": "https://publications.scilifelab.se/researcher/ee56816d10554890a6045b9705974854.json"}}, {"family": "Zhang", "given": "Chong", "initials": "C"}, {"family": "Miura", "given": "Kota", "initials": "K"}, {"family": "Colombelli", "given": "Julien", "initials": "J", "orcid": "0000-0002-2784-4276", "researcher": {"href": "https://publications.scilifelab.se/researcher/e64e92116688467197a2eaadb170eb12.json"}}], "type": "journal article", "published": "2021-04-26", "journal": {"title": "F1000Res", "issn": "2046-1402", "volume": "10", "pages": "320", "issn-l": "2046-1402"}, "abstract": "Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them. One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows. The emergence of \"big data\" in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows. These challenges are to reproducibly run workflows in remote environments, in particular when their components come from different software packages, but also to document them and link their parameters and results by following the FAIR principles (Findable, Accessible, Interoperable, Reusable) to foster open and reproducible science. In this opinion paper, we focus on giving some directions to the reader to tackle these challenges and navigate through this complex ecosystem, in order to find and use workflows, and to compare workflows addressing the same problem. We also discuss tools to run workflows in the cloud and on High Performance Computing resources, and suggest ways to make these workflows FAIR.", "doi": "10.12688/f1000research.52569.1", "pmid": "34136134", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC8182692"}], "notes": [], "created": "2021-11-29T12:35:24.298Z", "modified": "2021-11-29T12:35:24.493Z"}, {"entity": "publication", "iuid": "5aff66d54e10400292b4d4b1e1ee4a7d", "links": {"self": {"href": "https://publications.scilifelab.se/publication/5aff66d54e10400292b4d4b1e1ee4a7d.json"}, "display": {"href": "https://publications.scilifelab.se/publication/5aff66d54e10400292b4d4b1e1ee4a7d"}}, "title": "A workflow for high-throughput screening, data analysis, processing, and hit identification", "authors": [{"family": "Hansel", "given": "Catherine S", "initials": "CS"}, {"family": "Yousefian", "given": "Schayan", "initials": "S"}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J"}], "type": null, "published": "2020-11-30", "journal": {"title": "KNIME blog", "issn": null, "issn-l": null, "volume": "https://www.knime.com/blog/a-workflow-for-high-throughput-screening-data-analysis-processing-and-hit-identification", "issue": null, "pages": null}, "abstract": "High-throughput biochemical and phenotypic screening (HTS) is a gold standard technique for drug discovery. Using automation, the effects of thousands of compounds can be evaluated on cultured cells, or using biochemical in vitro assays. By doing so, \u201chit\u201d compounds can be identified that modulate the readout(s) favourably. Since HTS is typically conducted with large compound libraries under several conditions, the raw data generated is often very large and split over a number of spreadsheets/table-like sheets containing data. Therefore, we have created a KNIME workflow to help process and assess large sets of raw data generated from HTS. This workflow automatically imports HTS data and processes it to identify hits with tunable criteria. This means that the user is able to choose different thresholds to identify a compound considered as a hit. Additionally, three commonly used quality control measures, the Z-Prime, signal/background (S/B) and CV, are calculated in the workflow and are visualized in a comprehensive manner.", "doi": null, "pmid": null, "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2020-11-30T10:41:57.095Z", "modified": "2022-03-29T11:53:50.401Z"}, {"entity": "publication", "iuid": "fab53bcfddc94028843f584f4820ef3c", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fab53bcfddc94028843f584f4820ef3c.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fab53bcfddc94028843f584f4820ef3c"}}, "title": "TrackMate: My favorite image analysis tool, by Neubias members", "authors": [{"family": "Klemm", "given": "Anna", "initials": "A", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}], "type": null, "published": "2020-11-30", "journal": {"title": "Wiley Analytical Science", "issn": null, "issn-l": null, "volume": "https://analyticalscience.wiley.com/do/10.1002/was.000400044", "issue": null, "pages": null}, "abstract": "Time-lapse imaging allows researchers to follow various processes over time. Applications in life sciences can be as varied as evaluating the role of cell division and migration in developing embryos to the measurement of the moving pattern of living mice. In both of these example cases the process can be quantified using single particle tracking.\r\n\r\nA very powerful but still easy to use tool for single particle tracking is TrackMate \u2013 which comes as a plugin within Fiji [1,2]. This article gives an overview of the functionalities of TrackMate.", "doi": "10.1002/was.000400044", "pmid": null, "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2020-11-30T10:49:04.452Z", "modified": "2022-03-29T11:52:12.843Z"}, {"entity": "publication", "iuid": "eb542f0c8e904bd687fcec6c03eed0d5", "links": {"self": {"href": "https://publications.scilifelab.se/publication/eb542f0c8e904bd687fcec6c03eed0d5.json"}, "display": {"href": "https://publications.scilifelab.se/publication/eb542f0c8e904bd687fcec6c03eed0d5"}}, "title": "Automated identification of the mouse brain's spatial compartments from in situ sequencing data.", "authors": [{"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications.scilifelab.se/researcher/cca1e7c3e70a4d45aacc3a46d14b9cbe.json"}}, {"family": "Hilscher", "given": "Markus M", "initials": "MM"}, {"family": "Milli", "given": "Giorgia", "initials": "G"}, {"family": "Solorzano", "given": "Leslie", "initials": "L"}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2020-10-19", "journal": {"title": "BMC Biol.", "issn": "1741-7007", "issn-l": "1741-7007", "volume": "18", "issue": "1", "pages": "144"}, "abstract": "Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments.\r\n\r\nHere, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles.\r\n\r\nWe show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.", "doi": "10.1186/s12915-020-00874-5", "pmid": "33076915", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics Support for Computational Resources": "Service", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "10.1186/s12915-020-00874-5"}, {"db": "pmc", "key": "PMC7574211"}], "notes": [], "created": "2020-11-30T10:24:03.397Z", "modified": "2024-01-16T13:48:41.550Z"}, {"entity": "publication", "iuid": "132799a787034e5d88aa1fc990e7ef2a", "links": {"self": {"href": "https://publications.scilifelab.se/publication/132799a787034e5d88aa1fc990e7ef2a.json"}, "display": {"href": "https://publications.scilifelab.se/publication/132799a787034e5d88aa1fc990e7ef2a"}}, "title": "Tracking Microscope Performance: A Workflow to Compare Point Spread Function Evaluations Over Time.", "authors": [{"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Thomae", "given": "Andreas W", "initials": "AW", "orcid": "0000-0003-3156-9075", "researcher": {"href": "https://publications.scilifelab.se/researcher/65253ccd53bc44b2984f1669e4afdd8b.json"}}, {"family": "Wachal", "given": "Katarina", "initials": "K"}, {"family": "Dietzel", "given": "Steffen", "initials": "S", "orcid": "0000-0001-5725-3242", "researcher": {"href": "https://publications.scilifelab.se/researcher/ac46408300f54301b52a3f3525ac1e5b.json"}}], "type": "journal article", "published": "2019-06-00", "journal": {"title": "Microsc Microanal", "issn": "1435-8115", "issn-l": "1431-9276", "volume": "25", "issue": "3", "pages": "699-704"}, "abstract": "Routine system checks are essential for supervising the performance of an advanced light microscope. Recording and evaluating the point spread function (PSF) of a given system provides information about the resolution and imaging. We compared the performance of fluorescent and gold beads for PSF recordings. We then combined the open-source evaluation software PSFj with a newly developed KNIME pipeline named PSFtracker to create a standardized workflow to track a system's performance over several measurements and thus over long time periods. PSFtracker produces example images of recorded PSFs, plots full-width-half-maximum (FWHM) measurements over time and creates an html file which embeds the images and plots, together with a table of results. Changes of the PSF over time are thus easily spotted, either in FWHM plots or in the time series of bead images which allows recognition of aberrations in the shape of the PSF. The html file, viewed in a local browser or uploaded on the web, therefore provides intuitive visualization of the state of the PSF over time. In addition, uploading of the html file on the web allows other microscopists to compare such data with their own.", "doi": "10.1017/S1431927619000060", "pmid": "30722807", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "S1431927619000060"}], "notes": [], "created": "2019-12-16T09:54:05.272Z", "modified": "2022-03-29T11:58:58.911Z"}, {"entity": "publication", "iuid": "fe38a9e085de4033b895eda33e1ecee9", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fe38a9e085de4033b895eda33e1ecee9.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fe38a9e085de4033b895eda33e1ecee9"}}, "title": "Deep Learning in Image Cytometry: A Review.", "authors": [{"family": "Gupta", "given": "Anindya", "initials": "A", "orcid": "0000-0003-3557-4947", "researcher": {"href": "https://publications.scilifelab.se/researcher/f5ba336ef3ab4d819a2f367e0e4f988c.json"}}, {"family": "Harrison", "given": "Philip J", "initials": "PJ", "orcid": "0000-0003-4046-9017", "researcher": {"href": "https://publications.scilifelab.se/researcher/bbabe660124f4d96bcc4f604f61e569e.json"}}, {"family": "Wieslander", "given": "H\u00e5kan", "initials": "H", "orcid": "0000-0002-6289-7285", "researcher": {"href": "https://publications.scilifelab.se/researcher/a271478f5e2c407f9e5f9aceb6b92e10.json"}}, {"family": "Pielawski", "given": "Nicolas", "initials": "N", "orcid": "0000-0001-8182-0091", "researcher": {"href": "https://publications.scilifelab.se/researcher/a8bf24da074a451fa245ff20f07e4b48.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications.scilifelab.se/researcher/da3da754a0264d538a98d8a85874aec1.json"}}, {"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications.scilifelab.se/researcher/cca1e7c3e70a4d45aacc3a46d14b9cbe.json"}}, {"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications.scilifelab.se/researcher/24c62ca6579b4055a95d0dba53722939.json"}}, {"family": "Suveer", "given": "Amit", "initials": "A", "orcid": "0000-0002-7779-094X", "researcher": {"href": "https://publications.scilifelab.se/researcher/2079df9478364042935868f40b69b113.json"}}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications.scilifelab.se/researcher/605dbd52684d4e54ae4150a9933abe6e.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2019-04-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922", "volume": "95", "issue": "4", "pages": "366-380"}, "abstract": "Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. \u00a9 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.23701", "pmid": "30565841", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC6590257"}], "notes": [], "created": "2018-12-30T16:32:19.557Z", "modified": "2023-06-19T12:59:01.277Z"}]}