Classification of Human Epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors

Wiliem, Arnold; Wong, Yongkang; Sanderson, Conrad; Hobson, Peter; Chen, Shaokang; Lovell, Brian C.

doi:10.1109/wacv.2013.6475005

Cited by 65 publications

(73 citation statements)

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“…All features are densely extracted from the images and a dictionary was learned for each feature type. Feature are encoded using local linear coding, an efficient variant of sparse coding and max-pooling is used to aggregate the local linear codes; moreover, for each feature type, a 2-level cell pyramid is used to capture spatial structure, as in Wiliem et al (2013). Classification is done through an ensemble of multi-class linear SVM using the one-versus-all approach.…”

Section: Methods Facing Both Task 1 and Taskmentioning

confidence: 99%

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

Hobson

Lovell

Percannella

et al. 2016

Pattern Recognition Letters

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1 Research Highlights (Required)To create your highlights, please type the highlights against each \item command.It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• Updates the state of the art in HEp-2 cell and specimen image classification ABSTRACTThe Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial type 2 (HEp-2) cells is the current gold standard for the Antinuclear Antibody (ANA) test. The formulation of the diagnosis requires the visual analysis of a patient's specimen under a fluorescence microscope in order to recognize the cells' staining pattern which could be related to a connective tissue disease. This analysis is time consuming and error prone, thus in the recent past we have witnessed a growing interest in the pattern recognition scientific community directed at the development of methods for supporting this complex task. The main driver of the interest towards this problem is represented by the series of international benchmarking initiatives organized in the last four years that allowed dozens of research groups to propose innovative methodologies for HEp-2 cells' staining pattern classification. In this paper we update the state of the art on HEp-2 cells and specimens classification, by analyzing the performance achieved by the methods participating the contest on Performance Evaluation of IIF Image Analysis Systems, hosted by the 22nd edition of the International Conference on Pattern Recognition ICPR 2014, and to the Executable Thematic Special Issue of Pattern Recognition Letters on Pattern Recognition Techniques for IIF Images Analysis, and by highlighting the trends in the design of the best performing methods.

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Section: Methods Facing Both Task 1 and Taskmentioning

confidence: 99%

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

Hobson

Lovell

Percannella

et al. 2016

Pattern Recognition Letters

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“…These provide a more objective analysis which could be incorpo-14 rated into the overall test results. In recent years, we have seen significantly 15 growing interest in developing such systems [2,[10][11][12][13][14][15][16][17][18][19][20]. Nevertheless, the use 16 of private datasets with non-standard evaluation protocols makes it di cult 17 to draw meaningful conclusions from the existing works.…”

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confidence: 99%

“…Nevertheless, the use 16 of private datasets with non-standard evaluation protocols makes it di cult 17 to draw meaningful conclusions from the existing works. Therefore, it is 18 critical to develop a standard evaluation platform in order to advance the 19 A c c e p t e d M a n u s c r i p t domain [2]. One notable example is the first contest initiative held in con-1 junction with the International Conference on Pattern Recognition (ICPR) 2 2012, here denoted ICPR2012Contest [2], which is then followed by publi-3 cations of a Pattern Recognition journal special issue on the same theme 4 [21].…”

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confidence: 99%

“…Although at first glance the number of cell 16 images may appear significant, larger numbers of images are required 17 to draw more meaningful conclusions [2]. In fact, the overall analysis is 18 mainly a↵ected by the number of specimen images, as the cell images 19 from the same specimen are similar. More specifically, the classes in 20 both training and test sets only have two or three specimen images, 21 thus, the evaluation protocol is limited to the variation generated from 22 two specimen images.…”

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confidence: 99%

“…The analysis starts by determining the specimen 17 positivity from the observed fluorescent signal. The guidelines established 18 by the Center of Disease Control and Prevention, Atlanta, Georgia (CDC) 19 suggest the use of a scoring system ranging from 0 to 4+ wherein 0 represents 20 negative (no fluorescent signal observed), and 4+ represents the strongest 21 positive (very bright fluorescent signal observed) [26]. As this process is sub-22 jective, it is possible to reduce the scoring system into merely determining 23 whether the fluorescence intensity level of the sample is positive, intermedi-24 ate or negative [12].…”

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Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset

Hobson

Lovell

Percannella

et al. 2015

Artificial Intelligence in Medicine

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human epithelial type 2 interphase cellsclassification methods on a very large dataset, Artificial Intelligence In Medicine (2015), http://dx.doi.org/10. 1016/j.artmed.2015.08.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractObjective. 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 identify connective tissue diseases such as systemic lupus erythematosus and Sjögren's syndrome. However, the method su↵ers from numerous issues such as being subjective, time consuming and labour intensive. This has been the main motivation for the development of various computer-aided diagnosis systems whose main task is to automatically classify a given cell image into one of the predefined classes.Methods and material. The benchmarking was performed in the form of an international competition held in conjunction with the International Conference of Image Processing in 2013: fourteen teams, composed of practitioners and researchers in this area, took part in the initiative. The system developed by each team was trained and tested on a very large HEp-2 cell dataset comprising over 68,000 images of HEp-2 cell. The dataset contains cells with six di↵erent staining patterns and two levels of fluorescence intensity. For each method we provide a brief description highlighting the design Results. The staining pattern recognition accuracy attained by the methods varies between 47.91% and slightly above 83.65%. However, the difference between the top performing method and the seventh ranked method is only 5%. In the paper, we also study the performance achieved by fusing the best methods, finding that a recognition rate of 85.60% is reached when the top seven methods are employed.Conclusions. We found that highest performance is obtained when using a strong classifier (typically a kernelised support vector machine) in conjunction with features extracted from local statistics. Furthermore, the misclassification profiles of the di↵erent methods highlight that some staining patterns are intrinsically more di cult to recognize. We also noted that performance is strongly a↵ected by the fluorescence intensity level. Thus, low accuracy is to be expected when analyzing low contrasted images.

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Artificial Intelligence and Deep Learning for Rheumatologists

McMaster

Bird

Liew³

et al. 2022

Arthritis & Rheumatology

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Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.

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Classification of Human Epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors

Cited by 65 publications

References 25 publications

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset

Artificial Intelligence and Deep Learning for Rheumatologists

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