Deep convolutional neural network based HEp-2 cell classification

Jia, Xibin; Shen, Linlin; Zhou, Xiande; Yu, Shiqi

doi:10.1109/icpr.2016.7899611

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…Accuracy (ACA) Handcrafted features-based approach [60] 86.61% LeNet-5-like CNN without transfer learning [16] 88.75% VGG-16-like network without transfer learning [21] 90.23% Transfer learning using the pretrained VGG-19 91.57% Transfer learning using the pretrained AlexNet 92.41% Transfer learning using the pretrained VGG-16 [32] 92.89% DCR-Net [25] 94.15% Transfer learning using the pretrained ResNet-50 94.36% Our proposed deep parallel residual nets without cross-modal transfer learning 94.79%…”

Section: Methodsmentioning

confidence: 99%

“…One of the pioneers works to adopt the convolutional neural network (CNN) for the HEp-2 cell classification was the method proposed by Foggia et al [2] at the International Conference on Pattern Recognition (ICPR) HEp-2 cells classification contest in 2012. Since then, multiple works have proposed the use of CNN models in many different ways [16][17][18][19][20][21]. Among the most noticeable, Li et al [22] have presented a customized CNN model, called the deep residual inception network (DRI-Net), which associates the residual connection from the ResNet [23] and the "Inception modules" utilized in the GoogleNet [24].…”

Section: Introductionmentioning

confidence: 99%

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A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning

Vununu

Lee

Kwon

2021

Sensors

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In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods prefer to tackle this task using the supervised learning paradigm. However, the necessity of having thousands of manually annotated examples constitutes a serious concern for the state-of-the-art HEp-2 cells classification methods. We present in this work a method that uses active learning in order to minimize the necessity of annotating the majority of the examples in the dataset. For this purpose, we use cross-modal transfer learning coupled with parallel deep residual networks. First, the parallel networks, which take simultaneously different wavelet coefficients as inputs, are trained in a fully supervised way by using a very small and already annotated dataset. Then, the trained networks are utilized on the targeted dataset, which is quite larger compared to the first one, using active learning techniques in order to only select the images that really need to be annotated among all the examples. The obtained results show that active learning, when mixed with an efficient transfer learning technique, can allow one to achieve a quite pleasant discrimination performance with only a few annotated examples in hands. This will help in building CAD systems by simplifying the burdensome task of labeling images while maintaining a similar performance with the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning

Vununu

Lee

Kwon

2021

Sensors

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“…Customized DNN based methods. A large number of customized DNN based methods [7,9,12,6,13,21,22,17,18,25,45] have been proposed in recent years, especially in the Methods that made partial modifications to the generic DNN models. Liu et al's method [13] is one of the very few to make partial modifications to the CAE for HEP2IC.…”

Section: Cell-level Hep-2 (Cl-hep2ic) Methods That Use Dnn As a Class...mentioning

confidence: 99%

“…Two areas of HEP2IC have received attention of researchers, namely, individual HEp-2 cell classification and HEp-2 speciemn classification. Notable progresses [7,8,9,10,11,12,6,13,14,15,16,17,18,19,20,21,22,23,24,25] have been made in individual HEp-2 cell classification (also known as cell-level HEP2IC). The HEp-2 specimen classification [26] (also known as specimen-level HEP2IC) is still a relatively new area of research.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based HEp-2 Image Classification: A Comprehensive Review

Rahman,

Wang,

Sun

et al. 2019

Preprint

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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 in two levels, namely, cell-level and specimen-level. Both levels are covered in this review. In each level, the methods are organized with a deep network usage based taxonomy. The core idea, notable achievements, and key advantages and weakness 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 an overview of the current state-of-the-arts and a discussion on novel opportunities and future research directions in this field. It is hoped that this paper would give readers a comprehensive reference of this novel, challenging, and thriving field.

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“…Despite its unpowerful results, their study revealed interesting observations about the important rule of rotation in HEp-2 cell image augmentation and the informative nature of their extracellular textures for classification learning. Bayramoglu et al [23] used AlexNet architecture with various approaches of preprocessing and data augmentation, and Jia et al [24] adopted a customized CNN model that shares the general structure of the VGG network [25] configuration.…”

Section: A Cnns For Hep-2 Cell Image Classificationmentioning

confidence: 99%

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

2021

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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, humansubjectivity, 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 GANsgenerated 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.INDEX TERMS Computer-aided diagnosis systems (CADs), Convolutional Neural Networks (CNNs), Data augmentation, Data diversity, Evaluation metrics, Generative adversarial networks (GANs), HEp-2 cell image classification.

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Deep convolutional neural network based HEp-2 cell classification

Cited by 18 publications

References 7 publications

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning

Deep Learning based HEp-2 Image Classification: A Comprehensive Review

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

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