Automated classification for HEp-2 cells based on linear local distance coding framework

Xu, Xiang; Lin, Feng; Ng, Carol; Leong, Khai Pang

doi:10.1186/s13640-015-0064-7

Cited by 12 publications

(7 citation statements)

References 29 publications

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“…Xu et al [21] presented a method based on linear local distance coding in which, starting from local features, a local distance vector transformation was used by Euclidean distance. Finally, linear coding and max pooling were used, both on the local distance vector and on the local features.…”

Section: Introductionmentioning

confidence: 99%

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

2019

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The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database.

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Section: Introductionmentioning

confidence: 99%

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

2019

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“…A different kind of statistical feature, known as the gray-level size zone matrix, has been employed as the principal feature representation in the work by Thibault et al [8] and the nearest-neighbor classifier was adopted for the discrimination part. The same statistical features have been fed to an SVM in the work by Wiliem et al [9], while a linear local distance coding method was used for extracting the features that were also utilized as the inputs of a linear SVM by Xu et al [10].…”

Section: Introductionmentioning

confidence: 99%

A Deep Feature Extraction Method for HEp-2 Cell Image Classification

2018

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The automated and accurate classification of the images portraying the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of many autoimmune diseases. The extreme intra-class variations of the HEp-2 cell images datasets drastically complicates the classification task. We propose in this work a classification framework that, unlike most of the state-of-the-art methods, uses a deep learning-based feature extraction method in a strictly unsupervised way. We propose a deep learning-based hybrid feature learning with two levels of deep convolutional autoencoders. The first level takes the original cell images as the inputs and learns to reconstruct them, in order to capture the features related to the global shape of the cells, and the second network takes the gradients of the images, in order to encode the localized changes in intensity (gray variations) that characterize each cell type. A final feature vector is constructed by combining the latent representations extracted from the two networks, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the type of the cell image. We have tested the discriminability of the proposed features on two of the most popular HEp-2 cell classification datasets, the SNPHEp-2 and ICPR 2016 datasets. 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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“…As our proposed AdaCoDT method is a generative version of the BoW framework, we also compare it with BoW representation. LSC algorithm is chosen in our experiments due to its computationally efficiency and superior performance for staining pattern classification [34,35,62]. In order to make the evaluation comprehensive, we also use the FK [12] 68.7% 70.4% RIC-LBP [36] 67.5% 67.6% LBP [47] 58.9% 59.2%…”

Section: Experiments Setupmentioning

confidence: 99%

“…The BoW framework is one of the most successful image classification framework [56,57]. Our previous work verified its effectiveness in HEp-2 cell classification [34,35]. It presents an…”

Section: Introductionmentioning

confidence: 98%

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Efficient feature extraction and classification for staining patterns of HEP-2 Cells

Xu¹

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The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to autoimmune diseases. The ANAs detection can be accomplished via indirect immunofluorescence (IIF) technique using human epithelial (HEp-2) cell as substrate in laboratory. Identification of IIF slide images is based on human visual inspection, but it suffers from serious problems due to subjective evaluation. Therefore, Computer-Aided Diagnosis (CAD) system for supporting doctors' diagnosis is essential.

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Automated classification for HEp-2 cells based on linear local distance coding framework

Cited by 12 publications

References 29 publications

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

A Deep Feature Extraction Method for HEp-2 Cell Image Classification

Efficient feature extraction and classification for staining patterns of HEP-2 Cells

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