Label‐Free Leukemia Monitoring by Computer Vision-Reference-Cited by-同舟云学术

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Label‐Free Leukemia Monitoring by Computer Vision

Published:2020-02-24 Issue:4 Volume:97 Page:407-414
ISSN:1552-4922
Container-title:Cytometry Part A
language:en
Short-container-title:Cytometry

Author:

Doan Minh¹,Case Marian²,Masic Dino²,Hennig Holger¹³,McQuin Claire¹,Caicedo Juan¹,Singh Shantanu¹^ORCID,Goodman Allen¹,Wolkenhauer Olaf³,Summers Huw D.⁴,Jamieson David²,Delft Frederik W²,Filby Andrew⁵,Carpenter Anne E.¹^ORCID,Rees Paul¹⁴,Irving Julie²

Affiliation:

1. Imaging Platform Broad Institute of MIT and Harvard Cambridge Massachusetts

2. Northern Institute for Cancer Research Newcastle University UK

3. Department of Systems Biology & Bioinformatics University of Rostock Rostock Germany

4. College of Engineering, Swansea University, Bay Campus Swansea SA1 8EN UK

5. Flow Cytometry Core Facility. Innovation, Methodology and Application Research Theme, Biosciences Institute, Newcastle University NE2 4HH UK

Funder

Biotechnology and Biological Sciences Research Council

Bloodwise

Division of Biological Infrastructure

National Institute of General Medical Sciences

North of England Children’s Cancer Research Fund

Publisher

Wiley

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cyto.a.23987

Reference33 articles.

1. Minimal residual disease–guided therapy in childhood acute lymphoblastic leukemia

2. Augmented post-remission therapy for a minimal residual disease-defined high-risk subgroup of children and young people with clinical standard-risk and intermediate-risk acute lymphoblastic leukaemia (UKALL 2003): a randomised controlled trial

3. Treatment reduction for children and young adults with low-risk acute lymphoblastic leukaemia defined by minimal residual disease (UKALL 2003): a randomised controlled trial

4. Flow-cytometric vs. -morphologic assessment of remission in childhood acute lymphoblastic leukemia: a report from the Children’s Oncology Group (COG)

5. Use of Minimal Residual Disease Assessment to Redefine Induction Failure in Pediatric Acute Lymphoblastic Leukemia

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3. Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia;International Journal of Molecular Sciences;2024-06-12

4. Optofluidic time‐stretch imaging flow cytometry with a real‐time storage rate beyond 5.9 GB/s;Cytometry Part A;2024-06-06

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