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Cited by in F6Publishing
For: Chandradevan R, Aljudi AA, Drumheller BR, Kunananthaseelan N, Amgad M, Gutman DA, Cooper LAD, Jaye DL. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Lab Invest 2020;100:98-109. [PMID: 31570774 DOI: 10.1038/s41374-019-0325-7] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Amgad M, Atteya L, Hussein H, Mohammed KH, Hafiz E, Elsebaie MAT, Mobadersany P, Manthey D, Gutman DA, Elfandy H, Cooper LAD. Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings. Bioinformatics 2021:btab670. [PMID: 34586355 DOI: 10.1093/bioinformatics/btab670] [Reference Citation Analysis]
2 El Achi H, Khoury JD. Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology. Cancers (Basel) 2020;12:E797. [PMID: 32224980 DOI: 10.3390/cancers12040797] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
3 Talwar V, Chufal KS, Joga S. Artificial Intelligence: A New Tool in Oncologist's Armamentarium. Indian J Med Paediatr Oncol 2021;42:511-7. [DOI: 10.1055/s-0041-1735577] [Reference Citation Analysis]
4 Rustia DJA, Chao J, Chiu L, Wu Y, Chung J, Hsu J, Lin T. Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method. J Appl Entomol 2021;145:206-22. [DOI: 10.1111/jen.12834] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Jin H, Fu X, Cao X, Sun M, Wang X, Zhong Y, Yang S, Qi C, Peng B, He X, He F, Jiang Y, Gao H, Li S, Huang Z, Li Q, Fang F, Zhang J. Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study. J Med Syst 2020;44:184. [PMID: 32894360 DOI: 10.1007/s10916-020-01654-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Iacobucci I, Mullighan CG. Prognostic mutation constellations in acute myeloid leukaemia and myelodysplastic syndrome. Curr Opin Hematol 2021;28:101-9. [PMID: 33427759 DOI: 10.1097/MOH.0000000000000629] [Reference Citation Analysis]
7 Finkelman BS, Meindl A, LaBoy C, Griffin B, Narayan S, Brancamp R, Siziopikou KP, Pincus JL, Blanco LZ Jr. Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma. Breast Dis 2021. [PMID: 34397396 DOI: 10.3233/BD-201011] [Reference Citation Analysis]
8 Duchmann M, Wagner-Ballon O, Boyer T, Cheok M, Fournier E, Guerin E, Fenwarth L, Badaoui B, Freynet N, Benayoun E, Lusina D, Garcia I, Gardin C, Fenaux P, Pautas C, Quesnel B, Turlure P, Terré C, Thomas X, Lambert J, Renneville A, Preudhomme C, Dombret H, Itzykson R, Cluzeau T. Machine learning identifies the independent role of dysplasia in the prediction of response to chemotherapy in AML. Leukemia 2021. [PMID: 34615986 DOI: 10.1038/s41375-021-01435-7] [Reference Citation Analysis]
9 Gräbel P, Özkan Ö, Crysandt M, Herwartz R, Baumann M, Klinkhammer BM, Boor P, Brümmendorf TH, Merhof D. State of the Art Cell Detection in Bone Marrow Whole Slide Images. J Pathol Inform 2021;12:36. [PMID: 34760333 DOI: 10.4103/jpi.jpi_71_20] [Reference Citation Analysis]
10 Tayebi RM, Mu Y, Dehkharghanian T, Ross C, Sur M, Foley R, Tizhoosh HR, Campbell CJV. Automated bone marrow cytology using deep learning to generate a histogram of cell types. Commun Med 2022;2. [DOI: 10.1038/s43856-022-00107-6] [Reference Citation Analysis]
11 Lee SMW, Shaw A, Simpson JL, Uminsky D, Garratt LW. Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation. Sci Rep 2021;11:16917. [PMID: 34413367 DOI: 10.1038/s41598-021-96067-3] [Reference Citation Analysis]
12 Eckardt JN, Bornhäuser M, Wendt K, Middeke JM. Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects. Blood Adv 2020;4:6077-85. [PMID: 33290546 DOI: 10.1182/bloodadvances.2020002997] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
13 Radakovich N, Cortese M, Nazha A. Acute myeloid leukemia and artificial intelligence, algorithms and new scores. Best Pract Res Clin Haematol 2020;33:101192. [PMID: 33038981 DOI: 10.1016/j.beha.2020.101192] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
14 Radakovich N, Nagy M, Nazha A. Artificial Intelligence in Hematology: Current Challenges and Opportunities. Curr Hematol Malig Rep. 2020;15:203-210. [PMID: 32239350 DOI: 10.1007/s11899-020-00575-4] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
15 De Vera Mudry MC, Martin J, Schumacher V, Venugopal R. Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy. Toxicol Pathol 2021;49:851-61. [PMID: 33371793 DOI: 10.1177/0192623320980674] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
16 Lee SY, Chen CME, Lim EYP, Shen L, Sathe A, Singh A, Sauer J, Taghipour K, Yip CYC. Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells. J Pathol Inform 2021;12:18. [PMID: 34221634 DOI: 10.4103/jpi.jpi_110_20] [Reference Citation Analysis]
17 Shouval R, Fein JA, Savani B, Mohty M, Nagler A. Machine learning and artificial intelligence in haematology. Br J Haematol 2021;192:239-50. [PMID: 32602593 DOI: 10.1111/bjh.16915] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
18 Smith MA, Westerling-Bui T, Wilcox A, Schwartz J. Screening For Bone Marrow Cellularity Changes in Cynomolgus Macaques in Toxicology Safety Studies Using Artificial Intelligence Models. Toxicol Pathol 2021;49:905-11. [PMID: 33397208 DOI: 10.1177/0192623320981560] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]