AIMC Topic: Lymphoma, Follicular

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Efficient annotation bootstrapping for cell identification in follicular lymphoma.

Computer methods and programs in biomedicine Mar 27, 2025
BACKGROUND AND OBJECTIVE: In the medical field of digital pathology, many tasks rely on visual assessments of tissue patterns or cells, presenting an opportunity to apply computer vision methods. However, acquiring a substantial number of annotations...
Humans Image Processing, Computer-Assisted Lymphoma, Follicular Algorithms Deep Learning
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Lymphoma triage from H&E using AI for improved clinical management.

Journal of clinical pathology Dec 18, 2024
AIMS: In routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialised haematopathology services is needed. This places a heavy burden on pathology services, causes del...
Lymphoma, Follicular Reproducibility of Results Referral and Consultation Artificial Intelligence Image Interpretation, Computer-Assisted Lymph Nodes Retrospective Studies Humans Lymphoma Triage
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Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

British journal of haematology Dec 3, 2024
Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytogenetics and molecular biology) provides comprehensive diagnostic understandings. However, MBN subtypi...
Lymphoma, B-Cell Neprilysin High-Throughput Nucleotide Sequencing Immunohistochemistry Lymphoma, Mantle-Cell Leukemia, Hairy Cell Lymphoma, Follicular CD5 Antigens East Asian People Mutation Flow Cytometry Bone Marrow Retrospective Studies Leukemia, Lymphocytic, Chronic, B-Cell Humans Machine Learning Waldenstrom Macroglobulinemia
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Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis.

Computers in biology and medicine Jun 18, 2024
Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular lymphoma, but there is substantial observer variation in assessment among hematopathologists. We aimed to perform quantitative morphological analysis of...
Humans Adult Male Middle Aged Lymphoma, Follicular Female Artificial Intelligence Aged Prognosis
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Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

Laboratory investigation; a journal of technical methods and pathology May 29, 2020
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 ...
Lymphoma, Large B-Cell, Diffuse Observer Variation Humans Neural Networks, Computer Deep Learning Histological Techniques Lymphoma, Follicular Diagnosis, Computer-Assisted Pathologists Algorithms Lymphoma Pseudolymphoma Image Interpretation, Computer-Assisted
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Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.

Archives of pathology & laboratory medicine Jan 2, 2022
CONTEXT.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relative...
Biopsy Bone Marrow Lymphoma, Follicular Leukemia, Lymphocytic, Chronic, B-Cell Deep Learning Retrospective Studies Humans Reproducibility of Results Machine Learning
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