308 related articles for article (PubMed ID: 29049291)
1. A deep convolutional neural network for classification of red blood cells in sickle cell anemia.
Xu M; Papageorgiou DP; Abidi SZ; Dao M; Zhao H; Karniadakis GE
PLoS Comput Biol; 2017 Oct; 13(10):e1005746. PubMed ID: 29049291
[TBL] [Abstract][Full Text] [Related]
2. Diagnosis support of sickle cell anemia by classifying red blood cell shape in peripheral blood images.
Delgado-Font W; Escobedo-Nicot M; González-Hidalgo M; Herold-Garcia S; Jaume-I-Capó A; Mir A
Med Biol Eng Comput; 2020 Jun; 58(6):1265-1284. PubMed ID: 32222951
[TBL] [Abstract][Full Text] [Related]
3. Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.
Praljak N; Iram S; Goreke U; Singh G; Hill A; Gurkan UA; Hinczewski M
PLoS Comput Biol; 2021 Nov; 17(11):e1008946. PubMed ID: 34843453
[TBL] [Abstract][Full Text] [Related]
4. A Review of Automated Methods for the Detection of Sickle Cell Disease.
Das PK; Meher S; Panda R; Abraham A
IEEE Rev Biomed Eng; 2020; 13():309-324. PubMed ID: 31107662
[TBL] [Abstract][Full Text] [Related]
5. Ex Vivo Activation of Red Blood Cell Senescence by Plasma from Sickle-Cell Disease Patients: Correlation between Markers and Adhesion Consequences during Acute Disease Events.
Chadebech P; Bodivit G; Di Liberto G; Jouard A; Vasseur C; Pirenne F; Bartolucci P
Biomolecules; 2021 Jun; 11(7):. PubMed ID: 34208829
[TBL] [Abstract][Full Text] [Related]
6. Quantifying the rheological and hemodynamic characteristics of sickle cell anemia.
Lei H; Karniadakis GE
Biophys J; 2012 Jan; 102(2):185-94. PubMed ID: 22339854
[TBL] [Abstract][Full Text] [Related]
7. Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.
Lin YH; Liao KY; Sung KB
J Biomed Opt; 2020 Nov; 25(11):. PubMed ID: 33188571
[TBL] [Abstract][Full Text] [Related]
8. Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease.
Zhang M; Li X; Xu M; Li Q
IEEE J Biomed Health Inform; 2020 Nov; 24(11):3095-3102. PubMed ID: 32749972
[TBL] [Abstract][Full Text] [Related]
9. Deep Convolution Neural Network for Malignancy Detection and Classification in Microscopic Uterine Cervix Cell Images.
P B S; Faruqi F; K S H; Kudva R
Asian Pac J Cancer Prev; 2019 Nov; 20(11):3447-3456. PubMed ID: 31759371
[TBL] [Abstract][Full Text] [Related]
10. A combined computational and experimental investigation of the filtration function of splenic macrophages in sickle cell disease.
Li G; Qiang Y; Li H; Li X; Buffet PA; Dao M; Karniadakis GE
PLoS Comput Biol; 2023 Dec; 19(12):e1011223. PubMed ID: 38091361
[TBL] [Abstract][Full Text] [Related]
11. Mathematical Analysis for the Flow of Sickle Red Blood Cells in Microvessels for Bio Medical Application.
Chaturvedi P; Shah SR
Yale J Biol Med; 2023 Mar; 96(1):13-21. PubMed ID: 37009195
[TBL] [Abstract][Full Text] [Related]
12. Integrated automated particle tracking microfluidic enables high-throughput cell deformability cytometry for red cell disorders.
Guruprasad P; Mannino RG; Caruso C; Zhang H; Josephson CD; Roback JD; Lam WA
Am J Hematol; 2019 Feb; 94(2):189-199. PubMed ID: 30417938
[TBL] [Abstract][Full Text] [Related]
13. Regulation of Active ICAM-4 on Normal and Sickle Cell Disease RBCs via AKAPs Is Revealed by AFM.
Zhang J; Abiraman K; Jones SM; Lykotrafitis G; Andemariam B
Biophys J; 2017 Jan; 112(1):143-152. PubMed ID: 28076805
[TBL] [Abstract][Full Text] [Related]
14. Deep ensemble learning enables highly accurate classification of stored red blood cell morphology.
Routt AH; Yang N; Piety NZ; Lu M; Shevkoplyas SS
Sci Rep; 2023 Feb; 13(1):3152. PubMed ID: 36823298
[TBL] [Abstract][Full Text] [Related]
15. High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.
Yi F; Park S; Moon I
J Biomed Opt; 2021 Mar; 26(3):. PubMed ID: 33686845
[TBL] [Abstract][Full Text] [Related]
16. Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation.
Wang Y; Cao Y
Med Phys; 2020 Jan; 47(1):142-151. PubMed ID: 31691975
[TBL] [Abstract][Full Text] [Related]
17. The Unique Magnetic Signature of Sickle Red Blood Cells: A Comparison Between the Red Blood Cells of Transfused and Non-Transfused Sickle Cell Disease Patients and Healthy Donors.
Weigand M; Gomez-Pastora J; Strayer J; Wu X; Choe H; Lu S; Plencner E; Landes K; Palmer A; Zborowski M; Desai P; Chalmers J
IEEE Trans Biomed Eng; 2022 Dec; 69(12):3582-3590. PubMed ID: 35544484
[TBL] [Abstract][Full Text] [Related]
18. MD/DPD Multiscale Framework for Predicting Morphology and Stresses of Red Blood Cells in Health and Disease.
Chang HY; Li X; Li H; Karniadakis GE
PLoS Comput Biol; 2016 Oct; 12(10):e1005173. PubMed ID: 27792725
[TBL] [Abstract][Full Text] [Related]
19. A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.
Huang W; Xue Y; Wu Y
PLoS One; 2019; 14(7):e0219369. PubMed ID: 31299053
[TBL] [Abstract][Full Text] [Related]
20. Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth.
Welch ML; McIntosh C; Purdie TG; Wee L; Traverso A; Dekker A; Haibe-Kains B; Jaffray DA
Phys Med Biol; 2020 Jan; 65(1):015005. PubMed ID: 31683260
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
