HEp-2 Cell Image Classification Research Articles

Studying the Applicability of Generative Adversarial Networks on HEp-2 Cell Image Augmentation

The Anti-Nuclear Antibodies (ANAs) testing is the primary serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted mainly by the Indirect Immunofluorescence (IIF) on Human Epithelial cell-substrate (HEp-2) protocol. However, due to its high variability, human-subjectivity, and low throughput, there is an insistent need to develop an efficient Computer-Aided Diagnosis system (CADs) to automate this protocol. Many recently proposed Convolutional Neural Networks (CNNs) demonstrated promising results in HEp-2 cell image classification, which is the main task of the HE-p2 IIF protocol. However, the lack of large labeled datasets is still the main challenge in this field. This work provides a detailed study of the applicability of using generative adversarial networks (GANs) algorithms as an augmentation method. Different types of GANs were employed to synthesize HEp-2 cell images to address the data scarcity problem. For systematic comparison, empirical quantitative metrics were implemented to evaluate different GAN models' performance of learning the real data representations. The results of this work showed that though the high visual similarity with the real images, GANs' capacity to generate diverse data is still limited. This deficiency in the generated data diversity is found to be of a crucial impact when used as a standalone method for augmentation. However, combining limited-size GANs-generated data with classic augmentation improves the classification accuracy across different variants of CNNs. Our results demonstrated a competitive performance for the overall classification accuracy and the mean class accuracy of the HEp-2 cell image classification task.

Deep learning based HEp-2 image classification: A comprehensive review.

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 which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods. These methods perform HEp-2 image classification at two levels, namely, cell-level and specimen-level. Both levels are covered in this review. At each level, the methods are organized with a deep network usage based taxonomy. The core idea, notable achievements, and key strengths and weaknesses of each method are critically analyzed. Furthermore, a concise review of the existing HEp-2 datasets that are commonly used in the literature is given. The paper ends with a discussion on novel opportunities and future research directions in this field. It is hoped that this paper would provide readers with a thorough reference of this novel, challenging, and thriving field.

A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification.

Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated task due to the heterogeneity of these cellular images. Hence, an automated classification scheme appears to be necessary. However, the majority of the available methods prefer to utilize the supervised learning approach for this problem. The need for thousands of images labelled manually can represent a difficulty with this approach. The first contribution of this work is to demonstrate that classifying HEp-2 cell images can also be done using the unsupervised learning paradigm. Unlike the majority of the existing methods, we propose here a deep learning scheme that performs both the feature extraction and the cells’ discrimination through an end-to-end unsupervised paradigm. We propose the use of a deep convolutional autoencoder (DCAE) that performs feature extraction via an encoding–decoding scheme. At the same time, we embed in the network a clustering layer whose purpose is to automatically discriminate, during the feature learning process, the latent representations produced by the DCAE. Furthermore, we investigate how the quality of the network’s reconstruction can affect the quality of the produced representations. We have investigated the effectiveness of our method on some benchmark datasets and we demonstrate here that the unsupervised learning, when done properly, performs at the same level as the actual supervised learning-based state-of-the-art methods in terms of accuracy.

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

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.

Analyzing features by SWLDA for the classification of HEp-2 cell images using GMM

In this paper, a system is introduced for automatic classification of Human Epithelial cells type 2 Patterns (HEp-2) in indirect immunofluorescence imaging. HEp-2 cell classification was performed using Step-Wise Linear Discriminant Analysis (SWLDA) and Gaussian Mixture Model (GMM). Images were first normalized. Then, binary, intensity, statistical, spectral, wavelet-based, Haralick, CLBP and Gabor features were extracted from the normalized images. The best features were then selected using SWLDA, and the GMM framework was used for classification. Two protocols were examined considering all data and divided data (into intermediate and positive groups). In the first protocol all data are modeled with one GMM and in the second protocol two GMM models are designed for intermediate and positive data. The methods were applied on the ICPR2012 and ICIP2013 datasets. For the ICPR2012 dataset, a third protocol was also proposed based on the results of the second protocol. The classification was evaluated using standard metrics. The comparative results show that our method outperformed previous works for the ICPR2012 dataset and intermediate for the ICIP2013 dataset.

Ensembles of dense and dense sampling descriptors for the HEp-2 cells classification problem

The classification of Human Epithelial (HEp-2) cells images, acquired through Indirect Immunofluorescence (IIF) microscopy, is an effective method to identify staining patterns in patient sera. Indeed it can be used for diagnostic purposes, in order to reveal autoimmune diseases. However, the automated classification of IIF HEp-2 cell patterns represents a challenging task, due to the large intra-class and the small inter-class variability. Consequently, recent HEp-2 cell classification contests have greatly spurred the development of new IIF image classification systems.Here we propose an approach for the automatic classification of IIF HEp-2 cell images by fusion of several texture descriptors by ensemble of support vector machines combined by sum rule. Its effectiveness is evaluated using the HEp-2 cells dataset used for the “Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems” contest, hosted by the International Conference on Pattern Recognition in 2014: the accuracy on the testing set is 79.85%.The same dataset was used to test an ensemble of ternary-encoded local phase quantization descriptors, built by perturbation approaches: the accuracy on the training set is 84.16%. Finally, this ensemble was validated on 14 additional datasets, obtaining the best performance on 11 datasets.Our MATLAB code is available at https://www.dei.unipd.it/node/2357.

HEp-2 cell image classification with multiple linear descriptors

The automatic classification of the HEp-2 cell stain patterns from indirect immunofluorescence images has attracted much attention recently. As an image classification problem, it can be well solved by the state-of-the-art bag-of-features (BoF) model as long as a suitable local descriptor is known. Unfortunately, for this special task, we have very limited knowledge of such a descriptor.In this paper, we explore the possibility of automatically learning the descriptor from the image data itself. Specifically, we assume that a local patch can be well described by a set of linear projections performed on its pixel values. Based on this assumption, both unsupervised and supervised approaches are explored for learning the projections. More importantly, we propose a multi-projection-multi-codebook scheme which creates multiple linear projection descriptors and multiple image representation channels with each channel corresponding to one descriptor. Through our analysis, we show that the image representation obtained by combining these different channels can be more discriminative than that obtained from a single-projection scheme. This analysis is further verified by our experimental study.We evaluate the proposed approach by strictly following the protocol suggested by the organizer of the 2012 HEp-2 cell classification contest which is hosted to compare the state-of-the-art methods for HEp-2 cell classification. In this paper, our system achieves 66.6% cell level classification accuracy which is just slightly lower than the best performance achieved in the HEp-2 cell classification contest. This result is impressive and promising considering that we only utilize a single type of feature (namely, linear projection coefficients of patch pixel values) which is learned from the image data.

Knowledge Extraction from HEp2 Dataset

Pattern recognition is the process of taking the raw data, extract the features and categorize the patterns based on the features. Pattern recognition has been widely used in the area of diseases diagnosis. Autoimmune diseases are proven to be connected with the occurrence of autoantibodies in patient serum. Antinuclear autoantibodies (ANAs) identification can be accomplished in a laboratory using indirect immunofluorescence (IIF) imaging. In this paper we present the results of image analysis image analysis, feature extraction and classification of HEp-2 cell images. The experimental studies are performed on images of HEp-2 cells (1). 

Mining knowledge for HEp-2 cell image classification

HEp-2 cells are used for the identification of antinuclear autoantibodies (ANAs). They allow for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. The identification of the patterns has recently been done manually by a human inspecting the slides with a microscope. In this paper, we present results on the analysis and classification of cells using image analysis and data mining techniques. Starting from a knowledge acquisition process with a human operator, we developed an image analysis and feature extraction algorithm. The collection of the dataset was done based on an expert’s image reading and based on the automatic extracted features. A dataset containing 132 features for each entry was set up and given to a data mining algorithm to find out the relevant features among this large feature set and to construct the classification knowledge. The classifier was evaluated by cross validation. The results gave the expert new insights into the necessary features and the classification knowledge and show the feasibility of an automated inspection system.

Image mining: issues, framework, a generic tool and its application to medical-image diagnosis

A tool and a methodology for data mining in picture-archiving systems are presented. It is intended to discover the relevant knowledge for picture analysis and diagnosis from the data base of image descriptions. Knowledge-engineering methods are used to obtain a list of attributes for symbolic image descriptions. An expert describes images according to this list and stores descriptions in the data base. Digital-image processing can be applied to improve imaging of specific image features, or to get expert-independent feature evaluation. Decision-tree induction is used to learn the expert knowledge, presented in the form of image descriptions in the data base. A constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for data mining and image processing is presented and its application to image mining is shown on the task of Hep-2 cell-image classification. However, the tool and the methodology are generic and can be used for other image-mining tasks. We applied the developed methodology of data mining in other medical tasks, such as in lung-nodule diagnosis in X-ray images, lymph-node diagnosis in MRI and investigation of breast MRI.