ArticleCancers2021
Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- Article
- Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Artificial Intelligence in Hematologic Malignancies: Opportunities, Challenges, and Clinical Integration.Cureus · 2026Review
- The Application of Artificial Intelligence-Based Bone Marrow Cell Analysis System in Pediatric Hematological Diseases.International journal of laboratory hematology · 2025Article
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- The Evolution and Recent Advances in Diagnostic Criteria for Idiopathic Multicentric Castleman Disease.American journal of hematology · 2025Review
- Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.BMC medical imaging · 2025Article
- Constructing multicancer risk cohorts using national data from medical helplines and secondary care.NPJ digital medicine · 2025Article
- Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Enhanced HoVerNet Optimization for Precise Nuclei Segmentation in Diffuse Large B-Cell Lymphoma.Diagnostics (Basel, Switzerland) · 2025Article
- Role of artificial intelligence in haematolymphoid diagnostics.Histopathology · 2025Review
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends.TheScientificWorldJournal · 2025Review
- Morphology and multiparameter flow cytometry combined for integrated lymphoma diagnosis on small volume samples: possibilities and limitations.Virchows Archiv : an international journal of pathology · 2024Review
- Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.PLoS computational biology · 2024Article
- Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.Veterinary sciences · 2024Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.