139 related articles for article (PubMed ID: 31313517)
1. Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning.
Su X; Yuan T; Wang Z; Song K; Li R; Yuan C; Kong B
Cytometry A; 2020 Jan; 97(1):24-30. PubMed ID: 31313517
[TBL] [Abstract][Full Text] [Related]
2. Automatic Classification of Label-Free Cells from Small Cell Lung Cancer and Poorly Differentiated Lung Adenocarcinoma with 2D Light Scattering Static Cytometry and Machine Learning.
Wei H; Xie L; Liu Q; Shao C; Wang X; Su X
Cytometry A; 2019 Mar; 95(3):302-308. PubMed ID: 30508271
[TBL] [Abstract][Full Text] [Related]
3. Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology.
Chen Q; Zhang J
J Med Syst; 2019 Mar; 43(5):127. PubMed ID: 30919127
[TBL] [Abstract][Full Text] [Related]
4. 2D light scattering static cytometry for label-free single cell analysis with submicron resolution.
Xie L; Yang Y; Sun X; Qiao X; Liu Q; Song K; Kong B; Su X
Cytometry A; 2015 Nov; 87(11):1029-37. PubMed ID: 26115102
[TBL] [Abstract][Full Text] [Related]
5. Label-free light-sheet microfluidic cytometry for the automatic identification of senescent cells.
Lin M; Liu Q; Liu C; Qiao X; Shao C; Su X
Biomed Opt Express; 2018 Apr; 9(4):1692-1703. PubMed ID: 29675311
[TBL] [Abstract][Full Text] [Related]
6. Light scattering pattern specific convolutional network static cytometry for label-free classification of cervical cells.
Liu S; Yuan Z; Qiao X; Liu Q; Song K; Kong B; Su X
Cytometry A; 2021 Jun; 99(6):610-621. PubMed ID: 33840152
[TBL] [Abstract][Full Text] [Related]
7. Pattern recognition cytometry for label-free cell classification by 2D light scattering measurements.
Su X; Liu S; Qiao X; Yang Y; Song K; Kong B
Opt Express; 2015 Oct; 23(21):27558-65. PubMed ID: 26480415
[TBL] [Abstract][Full Text] [Related]
8. Light-sheet-based 2D light scattering cytometry for label-free characterization of senescent cells.
Lin M; Qiao X; Liu Q; Shao C; Su X
Biomed Opt Express; 2016 Dec; 7(12):5170-5181. PubMed ID: 28018733
[TBL] [Abstract][Full Text] [Related]
9. Label-free and noninvasive optical detection of the distribution of nanometer-size mitochondria in single cells.
Su X; Qiu Y; Marquez-Curtis L; Gupta M; Capjack CE; Rozmus W; Janowska-Wieczorek A; Tsui YY
J Biomed Opt; 2011 Jun; 16(6):067003. PubMed ID: 21721824
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification.
Sun J; Wang L; Liu Q; Tárnok A; Su X
Biomed Opt Express; 2020 Nov; 11(11):6674-6686. PubMed ID: 33282516
[TBL] [Abstract][Full Text] [Related]
12. Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry.
Liu S; Chu R; Xie J; Song K; Su X
Cytometry A; 2023 Mar; 103(3):240-250. PubMed ID: 36028474
[TBL] [Abstract][Full Text] [Related]
13. Differentiation of normal and leukemic cells by 2D light scattering label-free static cytometry.
Xie L; Liu Q; Shao C; Su X
Opt Express; 2016 Sep; 24(19):21700-7. PubMed ID: 27661908
[TBL] [Abstract][Full Text] [Related]
14. Microscope-based label-free microfluidic cytometry.
Su X; Kirkwood SE; Gupta M; Marquez-Curtis L; Qiu Y; Janowska-Wieczorek A; Rozmus W; Tsui YY
Opt Express; 2011 Jan; 19(1):387-98. PubMed ID: 21263578
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy.
Guo B; Lei C; Kobayashi H; Ito T; Yalikun Y; Jiang Y; Tanaka Y; Ozeki Y; Goda K
Cytometry A; 2017 May; 91(5):494-502. PubMed ID: 28399328
[TBL] [Abstract][Full Text] [Related]
17. Label-Free Identification of White Blood Cells Using Machine Learning.
Nassar M; Doan M; Filby A; Wolkenhauer O; Fogg DK; Piasecka J; Thornton CA; Carpenter AE; Summers HD; Rees P; Hennig H
Cytometry A; 2019 Aug; 95(8):836-842. PubMed ID: 31081599
[TBL] [Abstract][Full Text] [Related]
18. Label-free flow cytometry of rare circulating tumor cell clusters in whole blood.
Vora N; Shekhar P; Esmail M; Patra A; Georgakoudi I
Sci Rep; 2022 Jun; 12(1):10721. PubMed ID: 35750889
[TBL] [Abstract][Full Text] [Related]
19. Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers.
Kawakami E; Tabata J; Yanaihara N; Ishikawa T; Koseki K; Iida Y; Saito M; Komazaki H; Shapiro JS; Goto C; Akiyama Y; Saito R; Saito M; Takano H; Yamada K; Okamoto A
Clin Cancer Res; 2019 May; 25(10):3006-3015. PubMed ID: 30979733
[TBL] [Abstract][Full Text] [Related]
20. Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning.
Nissim N; Dudaie M; Barnea I; Shaked NT
Cytometry A; 2021 May; 99(5):511-523. PubMed ID: 32910546
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
