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For: 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]
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8 van der Velden BH, Kuijf HJ, Gilhuijs KG, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis 2022;79:102470. [DOI: 10.1016/j.media.2022.102470] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
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12 Gómez-de-Mariscal E, García-López-de-Haro C, Ouyang W, Donati L, Lundberg E, Unser M, Muñoz-Barrutia A, Sage D. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nat Methods 2021;18:1192-5. [PMID: 34594030 DOI: 10.1038/s41592-021-01262-9] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
13 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]
14 de Rooij M, van Poppel H, Barentsz JO. Risk Stratification and Artificial Intelligence in Early Magnetic Resonance Imaging-based Detection of Prostate Cancer. Eur Urol Focus 2021:S2405-4569(21)00305-9. [PMID: 34922897 DOI: 10.1016/j.euf.2021.11.005] [Reference Citation Analysis]
15 Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022;8:768106. [DOI: 10.3389/fmolb.2021.768106] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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17 Alrafiah AR. Application and performance of artificial intelligence technology in cytopathology. Acta Histochem 2022;124:151890. [PMID: 35366580 DOI: 10.1016/j.acthis.2022.151890] [Reference Citation Analysis]
18 Renner H, Schöler HR, Bruder JM. Combining Automated Organoid Workflows With Artificial Intelligence-Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High-Throughput Screens for Parkinson's Disease and Beyond. Mov Disord 2021. [PMID: 34498298 DOI: 10.1002/mds.28775] [Reference Citation Analysis]
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21 Eschweiler D, Rethwisch M, Jarchow M, Koppers S, Stegmaier J. 3D fluorescence microscopy data synthesis for segmentation and benchmarking. PLoS One 2021;16:e0260509. [PMID: 34855812 DOI: 10.1371/journal.pone.0260509] [Reference Citation Analysis]
22 Lucas AM, Ryder PV, Li B, Cimini BA, Eliceiri KW, Carpenter AE. Open-source deep-learning software for bioimage segmentation. Mol Biol Cell 2021;32:823-9. [PMID: 33872058 DOI: 10.1091/mbc.E20-10-0660] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
23 Merino-Casallo F, Gomez-Benito MJ, Hervas-Raluy S, Garcia-Aznar JM. Unravelling cell migration: defining movement from the cell surface. Cell Adh Migr 2022;16:25-64. [PMID: 35499121 DOI: 10.1080/19336918.2022.2055520] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Liu Z, Jin L, Chen J, Fang Q, Ablameyko S, Yin Z, Xu Y. A survey on applications of deep learning in microscopy image analysis. Comput Biol Med 2021;134:104523. [PMID: 34091383 DOI: 10.1016/j.compbiomed.2021.104523] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
25 Wirth D, McCall A, Hristova K. Neural network strategies for plasma membrane selection in fluorescence microscopy images. Biophys J 2021;120:2374-85. [PMID: 33961865 DOI: 10.1016/j.bpj.2021.04.030] [Reference Citation Analysis]