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For: Gupta A, Harrison PJ, Wieslander H, Pielawski N, Kartasalo K, Partel G, Solorzano L, Suveer A, Klemm AH, Spjuth O, Sintorn IM, Wählby C. Deep Learning in Image Cytometry: A Review. Cytometry A 2019;95:366-80. [PMID: 30565841 DOI: 10.1002/cyto.a.23701] [Cited by in Crossref: 67] [Cited by in F6Publishing: 47] [Article Influence: 16.8] [Reference Citation Analysis]
Number Citing Articles
1 Su X. Multidisciplinary single-cell optical cytometry. Cytometry A 2021;99:1065-6. [PMID: 34779116 DOI: 10.1002/cyto.a.24513] [Reference Citation Analysis]
2 Javer A, Rittscher J, Sailem HZ. DeepScratch: Single-cell based topological metrics of scratch wound assays. Comput Struct Biotechnol J 2020;18:2501-9. [PMID: 33005312 DOI: 10.1016/j.csbj.2020.08.018] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Franceschini A, Costantini I, Pavone FS, Silvestri L. Dissecting Neuronal Activation on a Brain-Wide Scale With Immediate Early Genes. Front Neurosci 2020;14:569517. [PMID: 33192255 DOI: 10.3389/fnins.2020.569517] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
4 Hartmann FJ, Bendall SC. Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat Rev Rheumatol 2020;16:87-99. [PMID: 31892734 DOI: 10.1038/s41584-019-0338-z] [Cited by in Crossref: 43] [Cited by in F6Publishing: 39] [Article Influence: 14.3] [Reference Citation Analysis]
5 Sun J, Tárnok A, Su X. Deep Learning-Based Single-Cell Optical Image Studies. Cytometry A 2020;97:226-40. [PMID: 31981309 DOI: 10.1002/cyto.a.23973] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
6 Kim J, McKee JA, Fontenot JJ, Jung JP. Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration. Front Bioeng Biotechnol 2019;7:443. [PMID: 31998708 DOI: 10.3389/fbioe.2019.00443] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
7 Keyes TJ, Domizi P, Lo YC, Nolan GP, Davis KL. A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry. Cytometry A 2020;97:782-99. [PMID: 32602650 DOI: 10.1002/cyto.a.24158] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
8 Munro LJ, Kell DB. Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021;478:3685-721. [PMID: 34673920 DOI: 10.1042/BCJ20210535] [Reference Citation Analysis]
9 Honrado C, McGrath JS, Reale R, Bisegna P, Swami NS, Caselli F. A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry. Anal Bioanal Chem 2020;412:3835-45. [PMID: 32189012 DOI: 10.1007/s00216-020-02497-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
10 Alhudhaif A, Cömert Z, Polat K. Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm. PeerJ Comput Sci 2021;7:e405. [PMID: 33817048 DOI: 10.7717/peerj-cs.405] [Reference Citation Analysis]
11 Hallou A, Yevick HG, Dumitrascu B, Uhlmann V. Deep learning for bioimage analysis in developmental biology. Development 2021;148:dev199616. [PMID: 34490888 DOI: 10.1242/dev.199616] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Su X, Yuan T, Wang Z, Song K, Li R, Yuan C, Kong B. Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning. Cytometry A 2020;97:24-30. [PMID: 31313517 DOI: 10.1002/cyto.a.23865] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
13 Luo S, Nguyen KT, Nguyen BTT, Feng S, Shi Y, Elsayed A, Zhang Y, Zhou X, Wen B, Chierchia G, Talbot H, Bourouina T, Jiang X, Liu AQ. Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection. Cytometry A 2021. [PMID: 33550703 DOI: 10.1002/cyto.a.24321] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020;18:2312-25. [PMID: 32994890 DOI: 10.1016/j.csbj.2020.08.003] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 8.0] [Reference Citation Analysis]
15 Zhu X, Li X, Ong K, Zhang W, Li W, Li L, Young D, Su Y, Shang B, Peng L, Xiong W, Liu Y, Liao W, Xu J, Wang F, Liao Q, Li S, Liao M, Li Y, Rao L, Lin J, Shi J, You Z, Zhong W, Liang X, Han H, Zhang Y, Tang N, Hu A, Gao H, Cheng Z, Liang L, Yu W, Ding Y. Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears. Nat Commun 2021;12:3541. [PMID: 34112790 DOI: 10.1038/s41467-021-23913-3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
16 Jeon H, Wei M, Huang X, Yao J, Han W, Wang R, Xu X, Chen J, Sun L, Han J. Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring. Anal Chem 2022. [PMID: 35416029 DOI: 10.1021/acs.analchem.2c00906] [Reference Citation Analysis]
17 Wang ZJ, Walsh AJ, Skala MC, Gitter A. Classifying T cell activity in autofluorescence intensity images with convolutional neural networks. J Biophotonics 2020;13:e201960050. [PMID: 31661592 DOI: 10.1002/jbio.201960050] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
18 Mergenthaler P, Hariharan S, Pemberton JM, Lourenco C, Penn LZ, Andrews DW. Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning. PLoS Comput Biol 2021;17:e1008630. [PMID: 33617523 DOI: 10.1371/journal.pcbi.1008630] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Soetje B, Fuellekrug J, Haffner D, Ziegler WH. Application and Comparison of Supervised Learning Strategies to Classify Polarity of Epithelial Cell Spheroids in 3D Culture. Front Genet 2020;11:248. [PMID: 32292417 DOI: 10.3389/fgene.2020.00248] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
20 Azuri I, Rosenhek-Goldian I, Regev-Rudzki N, Fantner G, Cohen SR. The role of convolutional neural networks in scanning probe microscopy: a review. Beilstein J Nanotechnol 2021;12:878-901. [PMID: 34476169 DOI: 10.3762/bjnano.12.66] [Reference Citation Analysis]
21 Lee KCM, Guck J, Goda K, Tsia KK. Toward deep biophysical cytometry: prospects and challenges. Trends Biotechnol 2021:S0167-7799(21)00064-0. [PMID: 33895013 DOI: 10.1016/j.tibtech.2021.03.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Prangemeier T, Wildner C, Françani AO, Reich C, Koeppl H. Yeast cell segmentation in microstructured environments with deep learning. Biosystems 2021;:104557. [PMID: 34634444 DOI: 10.1016/j.biosystems.2021.104557] [Reference Citation Analysis]
23 Pratapa A, Doron M, Caicedo JC. Image-based cell phenotyping with deep learning. Curr Opin Chem Biol 2021;65:9-17. [PMID: 34023800 DOI: 10.1016/j.cbpa.2021.04.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
24 Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 2020;477:4559-80. [PMID: 33290527 DOI: 10.1042/BCJ20200781] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 7.0] [Reference Citation Analysis]
25 Berryman S, Matthews K, Lee JH, Duffy SP, Ma H. Image-based phenotyping of disaggregated cells using deep learning. Commun Biol 2020;3:674. [PMID: 33188302 DOI: 10.1038/s42003-020-01399-x] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
26 Pattarone G, Acion L, Simian M, Iarussi E. Learning deep features for dead and living breast cancer cell classification without staining. Sci Rep 2021;11:10304. [PMID: 33986434 DOI: 10.1038/s41598-021-89895-w] [Reference Citation Analysis]
27 Imamura K, Yada Y, Izumi Y, Morita M, Kawata A, Arisato T, Nagahashi A, Enami T, Tsukita K, Kawakami H, Nakagawa M, Takahashi R, Inoue H. Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells. Ann Neurol 2021;89:1226-33. [PMID: 33565152 DOI: 10.1002/ana.26047] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
28 Gupta A, Saar T, Martens O, Le Moullec Y, Sintorn I. Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks. Int J Imaging Syst Technol 2020;30:327-39. [DOI: 10.1002/ima.22373] [Reference Citation Analysis]
29 Matuszewski DJ, Sintorn IM. TEM virus images: Benchmark dataset and deep learning classification. Comput Methods Programs Biomed 2021;209:106318. [PMID: 34375851 DOI: 10.1016/j.cmpb.2021.106318] [Reference Citation Analysis]
30 Weissleder R, Lee H. Automated molecular-image cytometry and analysis in modern oncology. Nat Rev Mater 2020;5:409-22. [DOI: 10.1038/s41578-020-0180-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
31 Nagao Y, Sakamoto M, Chinen T, Okada Y, Takao D. Robust classification of cell cycle phase and biological feature extraction by image-based deep learning. Mol Biol Cell 2020;31:1346-54. [PMID: 32320349 DOI: 10.1091/mbc.E20-03-0187] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
32 Karacosta LG. From imaging a single cell to implementing precision medicine: an exciting new era. Emerg Top Life Sci 2021;5:837-47. [PMID: 34889448 DOI: 10.1042/ETLS20210219] [Reference Citation Analysis]
33 Arnaud-Sampaio VF, Rabelo ILA, Bento CA, Glaser T, Bezerra J, Coutinho-Silva R, Ulrich H, Lameu C. Using Cytometry for Investigation of Purinergic Signaling in Tumor-Associated Macrophages. Cytometry A 2020;97:1109-26. [PMID: 32633884 DOI: 10.1002/cyto.a.24035] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
34 Krause SW. On Its Way to Primetime: Artificial Intelligence in Flow Cytometry Diagnostics. Cytometry A 2020;97:990-3. [PMID: 32686266 DOI: 10.1002/cyto.a.24191] [Reference Citation Analysis]
35 Adachi H, Kawamura Y, Nakagawa K, Horisaki R, Sato I, Yamaguchi S, Fujiu K, Waki K, Noji H, Ota S. Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics. Cytometry A 2020;97:415-22. [PMID: 32115874 DOI: 10.1002/cyto.a.23989] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
36 Rajawat A, Tripathi S. Disease diagnostics using hydrodynamic flow focusing in microfluidic devices: Beyond flow cytometry. Biomed Eng Lett 2020;10:241-57. [PMID: 32431954 DOI: 10.1007/s13534-019-00144-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Winfree S. User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue. Front Physiol 2022;13:833333. [DOI: 10.3389/fphys.2022.833333] [Reference Citation Analysis]
38 Wieslander H, Gupta A, Bergman E, Hallström E, Harrison PJ. Learning to see colours: Biologically relevant virtual staining for adipocyte cell images. PLoS One 2021;16:e0258546. [PMID: 34653209 DOI: 10.1371/journal.pone.0258546] [Reference Citation Analysis]
39 Mochalova EN, Kotov IA, Lifanov DA, Chakraborti S, Nikitin MP. Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis. Biotechnol Bioeng 2021. [PMID: 34750809 DOI: 10.1002/bit.27986] [Reference Citation Analysis]
40 LaChance J, Cohen DJ. Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging. PLoS Comput Biol 2020;16:e1008443. [PMID: 33362219 DOI: 10.1371/journal.pcbi.1008443] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Makarkin M, Bratashov D. State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures. Micromachines (Basel) 2021;12:1558. [PMID: 34945408 DOI: 10.3390/mi12121558] [Reference Citation Analysis]
42 Hall MS, Decker JT, Shea LD. Towards systems tissue engineering: Elucidating the dynamics, spatial coordination, and individual cells driving emergent behaviors. Biomaterials 2020;255:120189. [PMID: 32569865 DOI: 10.1016/j.biomaterials.2020.120189] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
43 Dey P. The emerging role of deep learning in cytology. Cytopathology 2021;32:154-60. [PMID: 33222315 DOI: 10.1111/cyt.12942] [Reference Citation Analysis]
44 Plant AL, Halter M, Stinson J. Probing pluripotency gene regulatory networks with quantitative live cell imaging. Comput Struct Biotechnol J 2020;18:2733-43. [PMID: 33101611 DOI: 10.1016/j.csbj.2020.09.025] [Reference Citation Analysis]
45 Harrison PJ, Wieslander H, Sabirsh A, Karlsson J, Malmsjö V, Hellander A, Wählby C, Spjuth O. Deep-learning models for lipid nanoparticle-based drug delivery. Nanomedicine (Lond) 2021;16:1097-110. [PMID: 33949890 DOI: 10.2217/nnm-2020-0461] [Reference Citation Analysis]
46 LaBelle CA, Massaro A, Cortés-Llanos B, Sims CE, Allbritton NL. Image-Based Live Cell Sorting. Trends Biotechnol 2021;39:613-23. [PMID: 33190968 DOI: 10.1016/j.tibtech.2020.10.006] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
47 Dunn KW, Fu C, Ho DJ, Lee S, Han S, Salama P, Delp EJ. DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. Sci Rep 2019;9:18295. [PMID: 31797882 DOI: 10.1038/s41598-019-54244-5] [Cited by in Crossref: 23] [Cited by in F6Publishing: 13] [Article Influence: 7.7] [Reference Citation Analysis]
48 Agbleke AA, Amitai A, Buenrostro JD, Chakrabarti A, Chu L, Hansen AS, Koenig KM, Labade AS, Liu S, Nozaki T, Ovchinnikov S, Seeber A, Shaban HA, Spille JH, Stephens AD, Su JH, Wadduwage D. Advances in Chromatin and Chromosome Research: Perspectives from Multiple Fields. Mol Cell 2020;79:881-901. [PMID: 32768408 DOI: 10.1016/j.molcel.2020.07.003] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 7.5] [Reference Citation Analysis]
49 Yao Y, Smal I, Grigoriev I, Akhmanova A, Meijering E, Wren J. Deep-learning method for data association in particle tracking. Bioinformatics 2020;36:4935-41. [DOI: 10.1093/bioinformatics/btaa597] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
50 Li Z, Xiao Y, Peng J, Locke D, Holmes D, Li L, Hamilton S, Cook E, Myer L, Vanderwall D, Cloutier N, Siddiqui AM, Whitehead P, Bishop R, Zhao L, Cvijic ME. Quantifying drug tissue biodistribution by integrating high content screening with deep-learning analysis. Sci Rep 2020;10:14408. [PMID: 32873881 DOI: 10.1038/s41598-020-71347-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
51 Aida S, Okugawa J, Fujisaka S, Kasai T, Kameda H, Sugiyama T. Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks. Biomolecules. 2020;10. [PMID: 32575396 DOI: 10.3390/biom10060931] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
52 Steigele S, Siegismund D, Fassler M, Kustec M, Kappler B, Hasaka T, Yee A, Brodte A, Heyse S. Deep Learning-Based HCS Image Analysis for the Enterprise. SLAS Discov 2020;25:812-21. [PMID: 32432952 DOI: 10.1177/2472555220918837] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
53 Takagi S, Sakuma S, Morita I, Sugimoto E, Yamaguchi Y, Higuchi N, Inamoto K, Ariji Y, Ariji E, Murakami H. Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment. J Clin Med 2020;9:E3475. [PMID: 33126595 DOI: 10.3390/jcm9113475] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
54 Merah-Mourah F, Cohen SO, Haziot A. A Two-Stage Flow Cytometry Strategy to Distinguish Single Cells from Doublets in Heterogeneous Cell Mixtures and Improve Cell Cluster Identification: Application to Human Monocyte Subpopulations. Curr Protoc 2021;1:e229. [PMID: 34416100 DOI: 10.1002/cpz1.229] [Reference Citation Analysis]
55 Vali-Betts E, Krause KJ, Dubrovsky A, Olson K, Graff JP, Mitra A, Datta-Mitra A, Beck K, Tsirigos A, Loomis C, Neto AG, Adler E, Rashidi HH. Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models. J Pathol Inform 2021;12:5. [PMID: 34012709 DOI: 10.4103/jpi.jpi_69_20] [Reference Citation Analysis]
56 Qi Y, Ren H, Li H, Zhang D, Cui H, Weng J, Li G, Wang G, Li Y. Interaction energy prediction of organic molecules using deep tensor neural network. Chinese Journal of Chemical Physics 2021;34:112-24. [DOI: 10.1063/1674-0068/cjcp2009163] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]