AIMC Topic: Fluorescent Antibody Technique, Indirect

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Comparison of artificial intelligence applications and commercial system performances using selected ANA IIF images.

Immunologic research Apr 14, 2025
Accurate and accessible classification of anti-nuclear antibodies (ANA) through indirect immunofluorescence (IIF) imaging is crucial for diagnosing autoimmune diseases. However, many laboratories, particularly those with limited resources, lack acces...
Antibodies, Antinuclear Image Processing, Computer-Assisted Fluorescent Antibody Technique, Indirect Autoimmune Diseases Artificial Intelligence Sensitivity and Specificity Humans
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A Multi-task learning U-Net model for end-to-end HEp-2 cell image analysis.

Artificial intelligence in medicine Nov 20, 2024
Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) microscopy performed with human epithelial type 2 (HEp-2) cells as the substrate is the reference metho...
Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Fluorescent Antibody Technique, Indirect Antibodies, Antinuclear Autoimmune Diseases Cell Line Humans
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Deep Learning Fuzzy Inference: An Interpretable Model for Detecting Indirect Immunofluorescence Patterns Associated with Nasopharyngeal Cancer.

The American journal of pathology Jun 22, 2022
The detection of serum Epstein-Barr virus antibodies by immunofluorescence assay (IFA) is considered the gold standard screening test for nasopharyngeal cancer (NPC) in high-risk populations. Given the high survival rate after early detection in asym...
Nasopharyngeal Neoplasms Fluorescent Antibody Technique, Indirect Herpesvirus 4, Human Nasopharyngeal Carcinoma Epstein-Barr Virus Infections Deep Learning Humans
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Deep learning based HEp-2 image classification: A comprehensive review.

Medical image analysis Jul 7, 2020
Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst...
Fluorescent Antibody Technique, Indirect Humans Deep Learning Image Processing, Computer-Assisted
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HEp-2 Cell Image Classification With Deep Convolutional Neural Networks.

IEEE journal of biomedical and health informatics Feb 8, 2016
Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which...
Epithelial Cells Humans Pattern Recognition, Automated Coloring Agents Neural Networks, Computer Fluorescent Antibody Technique, Indirect Image Processing, Computer-Assisted Algorithms Cell Line
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Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset.

Artificial intelligence in medicine Aug 13, 2015
OBJECTIVE: This paper presents benchmarking results of human epithelial type 2 (HEp-2) interphase cell image classification methods on a very large dataset. The indirect immunofluorescence method applied on HEp-2 cells has been the gold standard to i...
Diagnosis, Computer-Assisted Pattern Recognition, Automated Epithelial Cells Interphase Support Vector Machine Fluorescent Antibody Technique, Indirect Connective Tissue Diseases Image Processing, Computer-Assisted Algorithms Humans Sensitivity and Specificity
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Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning.

Computers in biology and medicine Jun 1, 2025
Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classificati...
Humans Neural Networks, Computer Benchmarking Fluorescent Antibody Technique, Indirect Image Processing, Computer-Assisted Deep Learning
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