Revisiting HEp-2 Cell Image Classification
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TLDR
A framework to automate the identification of antigen patterns in the cell images is presented and suggests that the algorithm is comparable with the state-of-the-art approaches.Abstract:
The immune system in homo sapiens protects the body against diseases by identifying and attacking foreign pathogens. However, when the system misidentifies native cells as threats, it results in an auto-immune response. The auto-antibodies generated during this phenomenon may be identified through the indirect immunofluorescence test. An important constituent process of this test is the automated identification of antigen patterns in the cell images, which is the focus of this research. We perform a detailed literature review and present a framework to automate the identification of antigen patterns. The efficacy of the framework, demonstrated on the MIVIA ICPR 2012 HEp-2 Cell Contest and SNP HEp-2 Cell datasets, suggests that the algorithm is comparable with the state-of-the-art approaches.read more
Citations
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Feature analysis for automatic classification of HEp-2 florescence patterns: Computer-aided diagnosis of auto-immune diseases
Subarna Ghosh,Vipin Chaudhary +1 more
TL;DR: This work proposes feature extraction methods for automatic recognition of staining patterns of HEp-2 images to develop a Computer-Aided Diagnosis system and support the specialists' decision.
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A Deep Feature Extraction Method for HEp-2 Cell Image Classification
TL;DR: The results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning-based state-of-the-art methods in terms of discrimination.
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A deep learning AlexNet model for classification of red blood cells in sickle cell anemia
TL;DR: The proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model and shows that the cell's name classification prediction accuracy, sensitivity, specificity, and precision were achieved.
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A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification.
TL;DR: A deep learning scheme is proposed that performs both the feature extraction and the cells’ discrimination through an end-to-end unsupervised paradigm that uses a deep convolutional autoencoder (DCAE) that performs feature extraction via an encoding–decoding scheme.
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A Dynamic Learning Method for the Classification of the HEp-2 Cell Images
TL;DR: A dynamic learning process is conducted with different networks taking different input variations in parallel in order to efficiently homogenize the features extracted from the images that have different intensity levels.
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