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

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 lear...
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Gespeichert in:

Autor*in:	Caleb Vununu [verfasserIn] Suk-Hwan Lee [verfasserIn] Ki-Ryong Kwon [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	HEp-2 cell images classification computer-aided diagnosis deep learning active learning transfer learning pattern recognition

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 4, p 1469
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:4, p 1469

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21041469

Katalog-ID:	DOAJ08671158X

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callnumber-first	T - Technology
author	Caleb Vununu
spellingShingle	Caleb Vununu misc TP1-1185 misc HEp-2 cell images classification misc computer-aided diagnosis misc deep learning misc active learning misc transfer learning misc pattern recognition misc Chemical technology A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning
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topic_title	TP1-1185 A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning HEp-2 cell images classification computer-aided diagnosis deep learning active learning transfer learning pattern recognition
topic	misc TP1-1185 misc HEp-2 cell images classification misc computer-aided diagnosis misc deep learning misc active learning misc transfer learning misc pattern recognition misc Chemical technology
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title	A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning
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title_sort	classification method for the cellular images based on active learning and cross-modal transfer learning
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title_auth	A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning
abstract	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.
abstractGer	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.
abstract_unstemmed	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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A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning

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