ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2014
Histopathological image analysis for centroblasts classification through dimensionality reduction approaches.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A multimodal explainable ai framework to assist in the differential diagnosis of head and neck reactive follicular hyperplasia and follicular lymphoma: an international multicentre study.Virchows Archiv : an international journal of pathology · 2026Article
- Detection of centroblast cells in H&E stained whole slide image based on object detection.Frontiers in medicine · 2024Article
- A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia.Molecular therapy. Nucleic acids · 2022Article
- Digital pathology and artificial intelligence.The Lancet. Oncology · 2019Review
- T-Cell Clustering in Neoplastic Follicles of Follicular Lymphoma.Cancer microenvironment : official journal of the International Cancer Microenvironment Society · 2018Article
- Classification of follicular lymphoma: the effect of computer aid on pathologists grading.BMC medical informatics and decision making · 2015Article
- Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice.Disease models & mechanisms · 2015Article
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
We present two novel automated image analysis methods to differentiate centroblast (CB) cells from noncentroblast (non-CB) cells in digital images of H&E-stained tissues of follicular lymphoma. CB cells are often confused by similar looking cells within the tissue, therefore a system to help their classification is necessary. Our methods extract the discriminatory features of cells by approximating the intrinsic dimensionality from the subspace spanned by CB and non-CB cells. In the first method, discriminatory features are approximated with the help of singular value decomposition (SVD), whereas in the second method they are extracted using Laplacian Eigenmaps. Five hundred high-power field images were extracted from 17 slides, which are then used to compose a database of 213 CB and 234 non-CB region of interest images. The recall, precision, and overall accuracy rates of the developed methods were measured and compared with existing classification methods. Moreover, the reproducibility of both classification methods was also examined. The average values of the overall accuracy were 99.22% ± 0.75% and 99.07% ± 1.53% for COB and CLEM, respectively. The experimental results demonstrate that both proposed methods provide better classification accuracy of CB/non-CB in comparison with the state of the art methods.
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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.