ArticleLaboratory investigation; a journal of technical methods and pathology2020
Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.
Article in Laboratory investigation; a journal of technical methods and pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- 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
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Deep Learning on Histopathological Images Enables Accurate Diagnosis of Common B-Cell and Hodgkin Lymphoma.Cancers · 2026Article
- AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a Revised MZL-IPI Model.Diagnostics (Basel, Switzerland) · 2026Article
- [Analysis of clinical characteristics and delays in diagnosis of the lymphoma of sinonasal cavities].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2026Article
- Application of convolutional neural networks for histopathological diagnosis of feline low-grade T-cell lymphoma and lymphoplasmacytic enteritis in intestinal biopsies.Frontiers in veterinary science · 2026Article
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- AI in Digital Pathology for Diffuse Large B-cell Lymphoma: A Systematic Review of Diagnosis and Classification.Cureus · 2025Review
- Article
- A novel hybrid convolutional and transformer network for lymphoma classification.Scientific reports · 2025Article
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- From Morphology to Gene Expression Profiling in Mycosis Fungoides: Is It Still a Diagnostic Challenge?Diagnostics (Basel, Switzerland) · 2025Review
- [Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2025Review
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Role of artificial intelligence in haematolymphoid diagnostics.Histopathology · 2025Review
- Attention induction based on pathologist annotations for improving whole slide pathology image classifier.Journal of pathology informatics · 2025Article
- Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends.TheScientificWorldJournal · 2025Review
- Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma.Journal of pathology informatics · 2024Article
- Artificial intelligence-based differential diagnosis of orbital MALT lymphoma and IgG4 related ophthalmic disease using hematoxylin-eosin images.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
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Authors and funding
23 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including a computer-aided diagnosis are desired. This study aims to classify histopathological images of malignant lymphomas through deep learning, which is a computer algorithm and type of artificial intelligence (AI) technology. We prepared hematoxylin and eosin (H&E) slides of a lesion area from 388 sections, namely, 259 with diffuse large B-cell lymphoma, 89 with follicular lymphoma, and 40 with reactive lymphoid hyperplasia, and created whole slide images (WSIs) using a whole slide system. WSI was annotated in the lesion area by experienced hematopathologists. Image patches were cropped from the WSI to train and evaluate the classifiers. Image patches at magnifications of ×5, ×20, and ×40 were randomly divided into a test set and a training and evaluation set. The classifier was assessed using the test set through a cross-validation after training. The classifier achieved the highest levels of accuracy of 94.0%, 93.0%, and 92.0% for image patches with magnifications of ×5, ×20, and ×40, respectively, in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia. Comparing the diagnostic accuracies between the proposed classifier and seven pathologists, including experienced hematopathologists, using the test set made up of image patches with magnifications of ×5, ×20, and ×40, the best accuracy demonstrated by the classifier was 97.0%, whereas the average accuracy achieved by the pathologists using WSIs was 76.0%, with the highest accuracy reaching 83.3%. In conclusion, the neural classifier can outperform pathologists in a morphological evaluation. These results suggest that the AI system can potentially support the diagnosis of malignant lymphoma.
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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.