A review on conditional random fields as a sequential classifier in machine learning

Liliana, Dewi Yanti; Basaruddin, Chan

doi:10.1109/icecos.2017.8167121

Cited by 15 publications

(7 citation statements)

References 11 publications

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“…In likelihood maximization [25], a probabilistic model representing the probability of each class in function of the observation is given; the decision consists in choosing the most likely class. Such likelihood maximization is also used in the more recent Conditional Random Fields (CRF) for image segmentation [26] and for classification [27]. Another kind of statistical methods are the Bayesian methods in which we have a prior knowledge about the belonging to a class.…”

Section: Data Classificationmentioning

confidence: 99%

Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy

Brochet¹,

Lapuyade‐Lahorgue²,

Bougleux³

et al. 2021

Preprint

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Pulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE image sequence can have as much as 25% of the sequence being uninformative frames (i.e. pure-noise and motion artefacts). For future data analysis, these uninformative frames must be first removed from the sequence. Therefore, the objective of our work is to develop an automatic detection method of uninformative images in endomicroscopy images. We propose to take the detection problem as a classification one. Considering advantages of deep learning methods, a classifier based on CNN (Convolutional Neural Network) is designed with a new loss function based on Havrda-Charvat entropy which is a parametrical generalization of the Shannon entropy. We propose to use this formula to get a better hold on all sorts of data since it provides a model more stable than the Shannon entropy. Our method is tested on one POE dataset including 2947 distinct images, is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting.

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Section: Data Classificationmentioning

confidence: 99%

Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy

Brochet¹,

Lapuyade‐Lahorgue²,

Bougleux³

et al. 2021

Preprint

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“…Conditional Random Fields (CRFs), as an important and prevalent type of ML method, are designed for building probabilistic models to explicitly describe the correlation of the pixels or the patches being predicted and label sequence data. The CRFs are attractive in the field of ML because they allow achieving in various research fields, such as Name Entity Recognition Problem in Natural Language Processing [34], Information Mining [35], Behavior Analysis [36], Image and Computer Vision [37], and Biomedicine [38]. In recent years, with the rapid development of DL, the CRF models are usually utilized as an essential pipeline within the deep neural network in order to refine the image segmentation results.…”

Section: Applications Of Conditional Random Fieldsmentioning

confidence: 99%

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

Li¹,

Wu²,

Li³

et al. 2021

Preprint

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“…Behavior Analysis [14], Image and Computer Vision [15], and Biomedicine [16]. In recent year, with the rapid development of deep learning (DL), the CRF models are usually utilized as an essential pipeline within the deep neural network in order to refine the image segmentation results [7].…”

Section: B Conditional Random Fieldsmentioning

confidence: 99%

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

Li,

et al. 2020

Preprint

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Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. Among these methods, random field models play an indispensable role in improving the analysis performance. In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. Firstly, we introduce the background of two random fields and pathology images. Secondly, we summarize the basic mathematical knowledge of MRFs and CRFs from modelling to optimization. Then, a thorough review of the recent research on the MRFs and CRFs of pathology images analysis is presented. Finally, we investigate the popular methodologies in the related works and discuss the method migration among CAD field.

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A review on conditional random fields as a sequential classifier in machine learning

Cited by 15 publications

References 11 publications

Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy

Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

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