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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes
Andrea Acevedo, Anna Merino, Laura Boldú, et al.
Computers in Biology and Medicine (2021) Vol. 134, pp. 104479-104479
Closed Access | Times Cited: 23

Showing 23 citing articles:

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks
Kevin Barrera, José Rodellar, Santiago Alférez, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 240, pp. 107629-107629
Open Access | Times Cited: 13
An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder
Hani Alshahrani, Gunjan Sharma, Vatsala Anand, et al.
Life (2023) Vol. 13, Iss. 10, pp. 2091-2091
Open Access | Times Cited: 12
A deep learning approach for automatic recognition of abnormalities in the cytoplasm of neutrophils
Kevin Barrera, José Rodellar, Santiago Alférez, et al.
Computers in Biology and Medicine (2024) Vol. 178, pp. 108691-108691
Open Access | Times Cited: 4
Artificial intelligence in haematopathology: current perspective and future directions
Carlo Pescia, Anna M Sozanska, Emily Thomas, et al.
Diagnostic histopathology (2025)
Closed Access
A Deep Learning Model for the Automatic Recognition of Aplastic Anemia, Myelodysplastic Syndromes, and Acute Myeloid Leukemia Based on Bone Marrow Smear
Mei-Fang Wang, Chunxia Dong, Yan Gao, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 17
Artificial intelligence to empower diagnosis of myelodysplastic syndromes by multiparametric flow cytometry
Valentin Clichet, Delphine Lebon, Nicolas Chapuis, et al.
Haematologica (2023)
Open Access | Times Cited: 10
Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes
Hussein Awada, Carmelo Gurnari, Arda Durmaz, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 5, pp. 2802-2802
Open Access | Times Cited: 15
Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes
Abdulrahman Alhajahjeh, Aziz Nazha
Current Hematologic Malignancy Reports (2023) Vol. 19, Iss. 1, pp. 9-17
Closed Access | Times Cited: 8
Hematology and Machine Learning
Amrom E. Obstfeld
The Journal of Applied Laboratory Medicine (2022) Vol. 8, Iss. 1, pp. 129-144
Closed Access | Times Cited: 13
Hematology and Hematopathology Insights Powered by Machine Learning: Shaping the Future of Blood Disorder Management
Rahime Tajvidi Asr, Masoumeh Rahimi, Mohammad Hossein Pourasad, et al.
Iranian Journal of Blood and Cancer (2024) Vol. 16, Iss. 4, pp. 9-19
Closed Access | Times Cited: 2
Addressing the Long-Tailed Data Distribution in Bone Marrow Cell Identification through Class Balance Deep Classification Model
Rakesh Shankar Ghosh, Pintu Chandra Shill
(2024), pp. 1-7
Closed Access | Times Cited: 2
Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network
A. Balasundaram, Ayesha Shaik, Shivam Akhouri, et al.
Diagnostics (2022) Vol. 13, Iss. 1, pp. 112-112
Open Access | Times Cited: 10
Performance of the new MC‐80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600
Anna Merino, Javier Laguna, María Rodríguez‐García, et al.
International Journal of Laboratory Hematology (2023) Vol. 46, Iss. 1, pp. 72-82
Open Access | Times Cited: 6
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Raheleh Salehi, Ario Sadafi, Armin Gruber, et al.
Lecture notes in computer science (2022), pp. 739-748
Closed Access | Times Cited: 9
Deep learning applications in visual data for benign and malignant hematological conditions: a systematic review and visual glossary
Andrew Srisuwananukorn, Mohamed E. Salama, Alexander T. Pearson
Haematologica (2023) Vol. 108, Iss. 8, pp. 1993-2010
Open Access | Times Cited: 5
Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects
Amgad Mohamed Elshoeibi, Ahmed Badr, Basel Elsayed, et al.
Cancers (2023) Vol. 16, Iss. 1, pp. 65-65
Open Access | Times Cited: 5
Towards Cross-Domain Single Blood Cell Image Classification Via Large-Scale Lora-Based Segment Anything Model
Lingcong Cai, Yongcheng Li, Ying Lü, et al.
(2024), pp. 1-5
Open Access | Times Cited: 1
A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection
José Rodellar, Kevin Barrera, Santiago Alférez, et al.
Bioengineering (2022) Vol. 9, Iss. 5, pp. 229-229
Open Access | Times Cited: 6
Leukocytes Classification Methods: Effectiveness and Robustness in a Real Application Scenario
Lorenzo Putzu, Andrea Loddo
Lecture notes in computer science (2024), pp. 3-14
Closed Access
Myelodysplastic syndrome risk assessment using priority linked correlated feature set using ResNet50
Kattekota SriLakshmi, D. Venkata Lakshmi
Biomedical Signal Processing and Control (2024) Vol. 96, pp. 106597-106597
Closed Access
Deep Learning‐Based Blood Abnormalities Detection as a Tool for VEXAS Syndrome Screening
Cédric De Almeida Braga, Maxence Bauvais, Pierre Sujobert, et al.
International Journal of Laboratory Hematology (2024) Vol. 47, Iss. 1, pp. 120-129
Closed Access
Bewegung in die richtige Richtung: Diagnostik und Therapie myelodysplastischer Neoplasien
Freya Schulze, Katja Sockel
Trillium Krebsmedizin (2024) Vol. 33, Iss. 6, pp. 414-425
Closed Access
Interpreting Convolutional Neural Networks via Layer-Wise Relevance Propagation
Wohuan Jia, Shaoshuai Zhang, Yue Jiang, et al.
Lecture notes in computer science (2022), pp. 457-467
Closed Access
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