119 related articles for article (PubMed ID: 37276178)
1. DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning.
Timonen VA; Kerkelä E; Impola U; Penna L; Partanen J; Kilpivaara O; Arvas M; Pitkänen E
Cytometry A; 2023 Oct; 103(10):807-817. PubMed ID: 37276178
[TBL] [Abstract][Full Text] [Related]
2. Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry.
Lippeveld M; Knill C; Ladlow E; Fuller A; Michaelis LJ; Saeys Y; Filby A; Peralta D
Cytometry A; 2020 Mar; 97(3):308-319. PubMed ID: 31688997
[TBL] [Abstract][Full Text] [Related]
3. Label-free cell cycle analysis for high-throughput imaging flow cytometry.
Blasi T; Hennig H; Summers HD; Theis FJ; Cerveira J; Patterson JO; Davies D; Filby A; Carpenter AE; Rees P
Nat Commun; 2016 Jan; 7():10256. PubMed ID: 26739115
[TBL] [Abstract][Full Text] [Related]
4. Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
Chlis NK; Rausch L; Brocker T; Kranich J; Theis FJ
Nucleic Acids Res; 2020 Nov; 48(20):11335-11346. PubMed ID: 33119742
[TBL] [Abstract][Full Text] [Related]
5. A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics.
Otesteanu CF; Ugrinic M; Holzner G; Chang YT; Fassnacht C; Guenova E; Stavrakis S; deMello A; Claassen M
Cell Rep Methods; 2021 Oct; 1(6):100094. PubMed ID: 35474892
[TBL] [Abstract][Full Text] [Related]
6. Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.
Zhang Z; Chen X; Tang R; Zhu Y; Guo H; Qu Y; Xie P; Lian IY; Wang Y; Lo YH
Sci Rep; 2023 Nov; 13(1):20533. PubMed ID: 37996496
[TBL] [Abstract][Full Text] [Related]
7. An open-source solution for advanced imaging flow cytometry data analysis using machine learning.
Hennig H; Rees P; Blasi T; Kamentsky L; Hung J; Dao D; Carpenter AE; Filby A
Methods; 2017 Jan; 112():201-210. PubMed ID: 27594698
[TBL] [Abstract][Full Text] [Related]
8. Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms.
Wang Y; Huang Z; Wang X; Yang F; Yao X; Pan T; Li B; Chu J
Lab Chip; 2023 Aug; 23(16):3615-3627. PubMed ID: 37458395
[TBL] [Abstract][Full Text] [Related]
9. Revealing architectural order with quantitative label-free imaging and deep learning.
Guo SM; Yeh LH; Folkesson J; Ivanov IE; Krishnan AP; Keefe MG; Hashemi E; Shin D; Chhun BB; Cho NH; Leonetti MD; Han MH; Nowakowski TJ; Mehta SB
Elife; 2020 Jul; 9():. PubMed ID: 32716843
[TBL] [Abstract][Full Text] [Related]
10. In silico-labeled ghost cytometry.
Ugawa M; Kawamura Y; Toda K; Teranishi K; Morita H; Adachi H; Tamoto R; Nomaru H; Nakagawa K; Sugimoto K; Borisova E; An Y; Konishi Y; Tabata S; Morishita S; Imai M; Takaku T; Araki M; Komatsu N; Hayashi Y; Sato I; Horisaki R; Noji H; Ota S
Elife; 2021 Dec; 10():. PubMed ID: 34930522
[TBL] [Abstract][Full Text] [Related]
11. Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry.
Li Y; Mahjoubfar A; Chen CL; Niazi KR; Pei L; Jalali B
Sci Rep; 2019 Jul; 9(1):11088. PubMed ID: 31366998
[TBL] [Abstract][Full Text] [Related]
12. High-content video flow cytometry with digital cell filtering for label-free cell classification by machine learning.
Liu C; Wang Z; Jia J; Liu Q; Su X
Cytometry A; 2023 Apr; 103(4):325-334. PubMed ID: 36287146
[TBL] [Abstract][Full Text] [Related]
13. Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.
Ali MAS; Misko O; Salumaa SO; Papkov M; Palo K; Fishman D; Parts L
SLAS Discov; 2021 Oct; 26(9):1125-1137. PubMed ID: 34167359
[TBL] [Abstract][Full Text] [Related]
14. Multispectral imaging of hematopoietic cells: where flow meets morphology.
McGrath KE; Bushnell TP; Palis J
J Immunol Methods; 2008 Jul; 336(2):91-7. PubMed ID: 18539294
[TBL] [Abstract][Full Text] [Related]
15. Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.
Wieslander H; Gupta A; Bergman E; Hallström E; Harrison PJ
PLoS One; 2021; 16(10):e0258546. PubMed ID: 34653209
[TBL] [Abstract][Full Text] [Related]
16. Imaging flow cytometry: coping with heterogeneity in biological systems.
Barteneva NS; Fasler-Kan E; Vorobjev IA
J Histochem Cytochem; 2012 Oct; 60(10):723-33. PubMed ID: 22740345
[TBL] [Abstract][Full Text] [Related]
17. Label-free prediction of cell painting from brightfield images.
Cross-Zamirski JO; Mouchet E; Williams G; Schönlieb CB; Turkki R; Wang Y
Sci Rep; 2022 Jun; 12(1):10001. PubMed ID: 35705591
[TBL] [Abstract][Full Text] [Related]
18. PXPermute reveals staining importance in multichannel imaging flow cytometry.
Shetab Boushehri S; Kornivetc A; Winter DJE; Kazeminia S; Essig K; Schmich F; Marr C
Cell Rep Methods; 2024 Feb; 4(2):100715. PubMed ID: 38412831
[TBL] [Abstract][Full Text] [Related]
19. Deep imaging flow cytometry.
Huang K; Matsumura H; Zhao Y; Herbig M; Yuan D; Mineharu Y; Harmon J; Findinier J; Yamagishi M; Ohnuki S; Nitta N; Grossman AR; Ohya Y; Mikami H; Isozaki A; Goda K
Lab Chip; 2022 Mar; 22(5):876-889. PubMed ID: 35142325
[TBL] [Abstract][Full Text] [Related]
20. High-throughput label-free detection of DNA-to-RNA transcription inhibition using brightfield microscopy and deep neural networks.
Sauvat A; Cerrato G; Humeau J; Leduc M; Kepp O; Kroemer G
Comput Biol Med; 2021 Jun; 133():104371. PubMed ID: 33845268
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
