137 related articles for article (PubMed ID: 33092347)
21. Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier.
Mirmohammadi P; Ameri M; Shalbaf A
Phys Eng Sci Med; 2021 Jun; 44(2):433-441. PubMed ID: 33751420
[TBL] [Abstract][Full Text] [Related]
22. Spectral characteristics of acute lymphoblastic leukemia in childhood.
Katzilakis N; Stiakaki E; Papadakis A; Dimitriou H; Stathopoulos E; Markaki EA; Balas C; Kalmanti M
Leuk Res; 2004 Nov; 28(11):1159-64. PubMed ID: 15380339
[TBL] [Abstract][Full Text] [Related]
23. Convolutional Neural Networks for Recognition of Lymphoblast Cell Images.
Pansombut T; Wikaisuksakul S; Khongkraphan K; Phon-On A
Comput Intell Neurosci; 2019; 2019():7519603. PubMed ID: 31281337
[TBL] [Abstract][Full Text] [Related]
24. ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification.
Jawahar M; H S; L JA; Gandomi AH
Comput Biol Med; 2022 Sep; 148():105894. PubMed ID: 35940163
[TBL] [Abstract][Full Text] [Related]
25. Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital.
Kashef A; Khatibi T; Mehrvar A
Asian Pac J Cancer Prev; 2020 Nov; 21(11):3211-3219. PubMed ID: 33247677
[TBL] [Abstract][Full Text] [Related]
26. Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model.
Jiang Z; Dong Z; Wang L; Jiang W
Comput Intell Neurosci; 2021; 2021():7529893. PubMed ID: 34471407
[TBL] [Abstract][Full Text] [Related]
27. Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute lymphoblastic leukemia.
Zhan M; Chen ZB; Ding CC; Qu Q; Wang GQ; Liu S; Wen FQ
Leuk Lymphoma; 2021 Oct; 62(10):2502-2513. PubMed ID: 33899650
[TBL] [Abstract][Full Text] [Related]
28. Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification.
Rodrigues LF; Backes AR; Travençolo BAN; de Oliveira GMB
J Digit Imaging; 2022 Jun; 35(3):623-637. PubMed ID: 35199257
[TBL] [Abstract][Full Text] [Related]
29. Automated Flow Cytometric MRD Assessment in Childhood Acute B- Lymphoblastic Leukemia Using Supervised Machine Learning.
Reiter M; Diem M; Schumich A; Maurer-Granofszky M; Karawajew L; Rossi JG; Ratei R; Groeneveld-Krentz S; Sajaroff EO; Suhendra S; Kampel M; Dworzak MN;
Cytometry A; 2019 Sep; 95(9):966-975. PubMed ID: 31282025
[TBL] [Abstract][Full Text] [Related]
30. A convolutional neural network-based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction.
Anwar S; Alam A
Med Biol Eng Comput; 2020 Dec; 58(12):3113-3121. PubMed ID: 33159270
[TBL] [Abstract][Full Text] [Related]
31. Applying machine learning to identify pediatric patients with newly diagnosed acute lymphoblastic leukemia using administrative data.
Cao L; Huang YS; Getz KD; Seif AE; Ruiz J; Miller TP; Fisher BT; Aplenc R; Li Y
Pediatr Blood Cancer; 2024 Mar; 71(3):e30858. PubMed ID: 38189744
[TBL] [Abstract][Full Text] [Related]
32. Acute Leukemia Classification Using Transcriptional Profiles From Low-Cost Nanopore mRNA Sequencing.
Wang J; Bhakta N; Ayer Miller V; Revsine M; Litzow MR; Paietta E; Fedoriw Y; Roberts KG; Gu Z; Mullighan CG; Jones CD; Alexander TB
JCO Precis Oncol; 2022 Apr; 6():e2100326. PubMed ID: 35442720
[TBL] [Abstract][Full Text] [Related]
33. Classification of cell morphology with quantitative phase microscopy and machine learning.
Li Y; Di J; Wang K; Wang S; Zhao J
Opt Express; 2020 Aug; 28(16):23916-23927. PubMed ID: 32752380
[TBL] [Abstract][Full Text] [Related]
34. Quantifying imbalanced classification methods for leukemia detection.
Depto DS; Rizvee MM; Rahman A; Zunair H; Rahman MS; Mahdy MRC
Comput Biol Med; 2023 Jan; 152():106372. PubMed ID: 36516574
[TBL] [Abstract][Full Text] [Related]
35. [Morphometric and electrokinetic criteria of cell identification in acute lymphoblastic leukemia].
Muskhelishvili LV
Gematol Transfuziol; 1990 Nov; 35(11):20-2. PubMed ID: 2283021
[TBL] [Abstract][Full Text] [Related]
36. Cytopathological image analysis using deep-learning networks in microfluidic microscopy.
Gopakumar G; Hari Babu K; Mishra D; Gorthi SS; Sai Subrahmanyam GR
J Opt Soc Am A Opt Image Sci Vis; 2017 Jan; 34(1):111-121. PubMed ID: 28059233
[TBL] [Abstract][Full Text] [Related]
37. Update on Lymphoblastic Leukemia/Lymphoma.
Luca DC
Clin Lab Med; 2021 Sep; 41(3):405-416. PubMed ID: 34304772
[TBL] [Abstract][Full Text] [Related]
38. Plankton classification with high-throughput submersible holographic microscopy and transfer learning.
MacNeil L; Missan S; Luo J; Trappenberg T; LaRoche J
BMC Ecol Evol; 2021 Jun; 21(1):123. PubMed ID: 34134620
[TBL] [Abstract][Full Text] [Related]
39. Precursor T-cell acute lymphoblastic leukemia/lymphoblastic lymphoma and acute biphenotypic leukemias.
Han X; Bueso-Ramos CE
Am J Clin Pathol; 2007 Apr; 127(4):528-44. PubMed ID: 17369128
[TBL] [Abstract][Full Text] [Related]
40. Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks.
Zhu XL; Shen HB; Sun H; Duan LX; Xu YY
Int J Comput Assist Radiol Surg; 2022 Jul; 17(7):1303-1311. PubMed ID: 35290645
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
