HEp-2 Cell Image Classification With Deep Convolutional Neural Networks.
- Publisher:
- Institute of Electrical and Electronics Engineers
- Publication Type:
- Journal Article
- Citation:
- IEEE Journal of Biomedical and Health Informatics, 2017, 21, (2), pp. 416-428
- Issue Date:
- 2017-03
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
HEp-2_Cell_Image_Classification_With_Deep_Convolutional_Neural_Networks.pdf | 1.56 MB | Adobe PDF |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Gao, Z | |
dc.contributor.author | Wang, L | |
dc.contributor.author | Zhou, L | |
dc.contributor.author |
Zhang, J |
|
dc.date.accessioned | 2022-07-13T06:20:21Z | |
dc.date.available | 2022-07-13T06:20:21Z | |
dc.date.issued | 2017-03 | |
dc.identifier.citation | IEEE Journal of Biomedical and Health Informatics, 2017, 21, (2), pp. 416-428 | |
dc.identifier.issn | 2168-2208 | |
dc.identifier.issn | 2168-2208 | |
dc.identifier.uri | http://hdl.handle.net/10453/158859 | |
dc.description.abstract | 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 have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that 1) the proposed framework can effectively outperform existing models by properly applying data augmentation, 2) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers | |
dc.relation.ispartof | IEEE Journal of Biomedical and Health Informatics | |
dc.relation.isbasedon | 10.1109/JBHI.2016.2526603 | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | 08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences | |
dc.subject.classification | Medical Informatics | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Cell Line | |
dc.subject.mesh | Coloring Agents | |
dc.subject.mesh | Epithelial Cells | |
dc.subject.mesh | Fluorescent Antibody Technique, Indirect | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Image Processing, Computer-Assisted | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Pattern Recognition, Automated | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Cell Line | |
dc.subject.mesh | Coloring Agents | |
dc.subject.mesh | Epithelial Cells | |
dc.subject.mesh | Fluorescent Antibody Technique, Indirect | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Image Processing, Computer-Assisted | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Pattern Recognition, Automated | |
dc.subject.mesh | Cell Line | |
dc.subject.mesh | Epithelial Cells | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Fluorescent Antibody Technique, Indirect | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Image Processing, Computer-Assisted | |
dc.subject.mesh | Pattern Recognition, Automated | |
dc.subject.mesh | Coloring Agents | |
dc.subject.mesh | Neural Networks, Computer | |
dc.title | HEp-2 Cell Image Classification With Deep Convolutional Neural Networks. | |
dc.type | Journal Article | |
utslib.citation.volume | 21 | |
utslib.location.activity | United States | |
utslib.for | 08 Information and Computing Sciences | |
utslib.for | 09 Engineering | |
utslib.for | 11 Medical and Health Sciences | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute | |
utslib.copyright.status | closed_access | * |
pubs.consider-herdc | false | |
dc.date.updated | 2022-07-13T06:20:20Z | |
pubs.issue | 2 | |
pubs.publication-status | Published | |
pubs.volume | 21 | |
utslib.citation.issue | 2 |
Abstract:
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 have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that 1) the proposed framework can effectively outperform existing models by properly applying data augmentation, 2) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.
Please use this identifier to cite or link to this item:
Download statistics for the last 12 months
Not enough data to produce graph