These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
170 related articles for article (PubMed ID: 36853796)
1. An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears. Lewis JE; Shebelut CW; Drumheller BR; Zhang X; Shanmugam N; Attieh M; Horwath MC; Khanna A; Smith GH; Gutman DA; Aljudi A; Cooper LAD; Jaye DL Mod Pathol; 2023 Feb; 36(2):100003. PubMed ID: 36853796 [TBL] [Abstract][Full Text] [Related]
2. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Chandradevan R; Aljudi AA; Drumheller BR; Kunananthaseelan N; Amgad M; Gutman DA; Cooper LAD; Jaye DL Lab Invest; 2020 Jan; 100(1):98-109. PubMed ID: 31570774 [TBL] [Abstract][Full Text] [Related]
3. Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network. Hazra D; Byun YC; Kim WJ Comput Methods Programs Biomed; 2022 Sep; 224():107019. PubMed ID: 35878483 [TBL] [Abstract][Full Text] [Related]
4. Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence. Fu X; Fu M; Li Q; Peng X; Lu J; Fang F; Chen M Acta Cytol; 2020; 64(6):588-596. PubMed ID: 32721953 [TBL] [Abstract][Full Text] [Related]
5. The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks. Wang X; Wang Y; Qi C; Qiao S; Yang S; Wang R; Jin H; Zhang J Technol Cancer Res Treat; 2023; 22():15330338221150069. PubMed ID: 36700246 [TBL] [Abstract][Full Text] [Related]
6. Only prolonged time from abstraction found to affect viable nucleated cell concentrations in vertebral body bone marrow aspirate. Badrinath R; Bohl DD; Hustedt JW; Webb ML; Grauer JN Spine J; 2014 Jun; 14(6):990-5. PubMed ID: 24184640 [TBL] [Abstract][Full Text] [Related]
7. [Health technology assessment report: Computer-assisted Pap test for cervical cancer screening]. Della Palma P; Moresco L; Giorgi Rossi P Epidemiol Prev; 2012; 36(5 Suppl 3):e1-43. PubMed ID: 23139174 [TBL] [Abstract][Full Text] [Related]
8. Deep learning for bone marrow cell detection and classification on whole-slide images. Wang CW; Huang SC; Lee YC; Shen YJ; Meng SI; Gaol JL Med Image Anal; 2022 Jan; 75():102270. PubMed ID: 34710655 [TBL] [Abstract][Full Text] [Related]
9. Estimation of bone marrow cellularity using digital image nucleated cell counts in patients receiving chemotherapy. Kim Y; Kim M; Kim Y; Han JH; Han K Int J Lab Hematol; 2014 Oct; 36(5):548-54. PubMed ID: 24612511 [TBL] [Abstract][Full Text] [Related]
10. CDC-NET: a cell detection and confirmation network of bone marrow aspirate images for the aided diagnosis of AML. Su J; Liu Y; Zhang J; Han J; Song J Med Biol Eng Comput; 2024 Feb; 62(2):575-589. PubMed ID: 37953336 [TBL] [Abstract][Full Text] [Related]
11. Evaluation of an open-source machine-learning tool to quantify bone marrow plasma cells. Baranova K; Tran C; Plantinga P; Sangle N J Clin Pathol; 2021 Jul; 74(7):462-468. PubMed ID: 33952591 [TBL] [Abstract][Full Text] [Related]
12. Whole slide image representation in bone marrow cytology. Mu Y; Tizhoosh HR; Dehkharghanian T; Campbell CJV Comput Biol Med; 2023 Nov; 166():107530. PubMed ID: 37837726 [TBL] [Abstract][Full Text] [Related]
13. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. Choi JW; Ku Y; Yoo BW; Kim JA; Lee DS; Chai YJ; Kong HJ; Kim HC PLoS One; 2017; 12(12):e0189259. PubMed ID: 29228051 [TBL] [Abstract][Full Text] [Related]
14. A Machine Learning Tool Using Digital Microscopy (Morphogo) for the Identification of Abnormal Lymphocytes in the Bone Marrow. Tang G; Fu X; Wang Z; Chen M Acta Cytol; 2021; 65(4):354-357. PubMed ID: 34350848 [TBL] [Abstract][Full Text] [Related]
15. Proximal Humerus and Ilium Are Reliable Sources of Bone Marrow Aspirates for Biologic Augmentation During Arthroscopic Surgery. Otto A; Muench LN; Kia C; Baldino JB; Mehl J; Dyrna F; Voss A; McCarthy MB; Nazal MR; Martin SD; Mazzocca AD Arthroscopy; 2020 Sep; 36(9):2403-2411. PubMed ID: 32554079 [TBL] [Abstract][Full Text] [Related]
16. Digital assessment of peripheral blood and bone marrow aspirate smears. Lewis JE; Pozdnyakova O Int J Lab Hematol; 2023 Jun; 45 Suppl 2():50-58. PubMed ID: 37211430 [TBL] [Abstract][Full Text] [Related]
17. Recent advancements in machine learning for bone marrow cell morphology analysis. Lin Y; Chen Q; Chen T Front Med (Lausanne); 2024; 11():1402768. PubMed ID: 38947236 [TBL] [Abstract][Full Text] [Related]
18. Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma. Feng M; Deng Y; Yang L; Jing Q; Zhang Z; Xu L; Wei X; Zhou Y; Wu D; Xiang F; Wang Y; Bao J; Bu H Diagn Pathol; 2020 May; 15(1):65. PubMed ID: 32471471 [TBL] [Abstract][Full Text] [Related]
19. Automated hematologic analysis of bone marrow aspirate samples from healthy Beagle dogs. Tan E; Abrams-Ogg AC; Defarges A; Bienzle D Vet Clin Pathol; 2014 Sep; 43(3):342-51. PubMed ID: 25135758 [TBL] [Abstract][Full Text] [Related]
20. The challenging task of enumerating blasts in the bone marrow. Hodes A; Calvo KR; Dulau A; Maric I; Sun J; Braylan R Semin Hematol; 2019 Jan; 56(1):58-64. PubMed ID: 30573046 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]