Machine learning and artificial intelligence in haematology

R Shouval, JA Fein, B Savani, M Mohty… - British journal of …, 2021 - Wiley Online Library
Digitalization of the medical record and integration of genomic methods into clinical practice
have resulted in an unprecedented wealth of data. Machine learning is a subdomain of …

Machine learning and deep learning applications in multiple myeloma diagnosis, prognosis, and treatment selection

A Allegra, A Tonacci, R Sciaccotta, S Genovese… - Cancers, 2022 - mdpi.com
Simple Summary Multiple myeloma is a malignant neoplasm of plasma cells with complex
pathogenesis. With major progresses in multiple myeloma research, it is essential that we …

Deep learning for bone marrow cell detection and classification on whole-slide images

CW Wang, SC Huang, YC Lee, YJ Shen, SI Meng… - Medical Image …, 2022 - Elsevier
Bone marrow (BM) examination is an essential step in both diagnosing and managing
numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of …

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer

M Amgad, LA Atteya, H Hussein, KH Mohammed… - …, 2022 - academic.oup.com
Background Deep learning enables accurate high-resolution mapping of cells and tissue
structures that can serve as the foundation of interpretable machine-learning models for …

Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method

DJA Rustia, JJ Chao, LY Chiu, YF Wu… - Journal of applied …, 2021 - Wiley Online Library
Inspection of insect sticky paper traps is an essential task for an effective integrated pest
management (IPM) programme. However, identification and counting of the insect pests …

Automated bone marrow cytology using deep learning to generate a histogram of cell types

RM Tayebi, Y Mu, T Dehkharghanian, C Ross… - Communications …, 2022 - nature.com
Background Bone marrow cytology is required to make a hematological diagnosis,
influencing critical clinical decision points in hematology. However, bone marrow cytology is …

Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning

P Manescu, P Narayanan, C Bendkowski, M Elmi… - Scientific Reports, 2023 - nature.com
While optical microscopy inspection of blood films and bone marrow aspirates by a
hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low …

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

JN Eckardt, M Bornhäuser, K Wendt… - Blood …, 2020 - ashpublications.org
Abstract Machine learning (ML) is rapidly emerging in several fields of cancer research. ML
algorithms can deal with vast amounts of medical data and provide a better understanding of …

[HTML][HTML] Computational pathology: a survey review and the way forward

MS Hosseini, BE Bejnordi, VQH Trinh, L Chan… - Journal of Pathology …, 2024 - Elsevier
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

Deep learning identifies acute promyelocytic leukemia in bone marrow smears

JN Eckardt, T Schmittmann, S Riechert, M Kramer… - BMC cancer, 2022 - Springer
Background Acute promyelocytic leukemia (APL) is considered a hematologic emergency
due to high risk of bleeding and fatal hemorrhages being a major cause of death. Despite …