Deep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy

Brochet, Thibaud; Lapuyade‐Lahorgue, Jérôme; Bougleux, Sébastien; Ruan, Su

doi:10.1016/j.irbm.2021.06.006

Cited by 10 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lebesgue measure for continuous case) by a Radon-Nykodim derivative between probability measures. Shannon's entropy can be generalized on other entropies such as Renyi [15] and Tsallis-Havrda-Charvat [16,17]. In this paper, we are interested in a particular generalization of Shannon cross-entropy: Tsallis-Havrda-Charvat cross-entropy [18].…”

Section: Introductionmentioning

confidence: 99%

“…[24], the maximization of the entropy measure is studied for different classes of entropies such as Tsallis-Havrda-Charvat; the maximization of Tsallis-Havrda-Charvat entropy under constraints appears to be a way to generalize Gaussian distributions. In our previous work [17], Tsallis-Havrda-Charvat cross-entropy is used for the detection of noisy images in pulmonary microendoscopy. To capitalize and improve on this previous work, we propose to use deep learning in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have been carried out using CT scan images and clinical data. To our knowledge, there is no article using Tsallis-Havrda-Charvat for recurrence prediction [17]. The novelty of this article lies in the performance comparison of Shannon and Tsallis-Havrda-Charvat entropies in the context of cancer recurrence prediction with CT scan data combined with clinical information for patients affected by head and neck (H&N) or lung cancers (examples of the analyzed CT images are displayed in Figure 1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction

Brochet

Lapuyade‐Lahorgue

Huat

et al. 2022

Entropy

View full text Add to dashboard Cite

In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis–Havrda–Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications. Shannon cross-entropy is widely used as a loss function for most neural networks applied to the segmentation, classification and detection of images. Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy. In this work, we compare these two entropies through a medical application for predicting recurrence in patients with head–neck and lung cancers after treatment. Based on both CT images and patient information, a multitask deep neural network is proposed to perform a recurrence prediction task using cross-entropy as a loss function and an image reconstruction task. Tsallis–Havrda–Charvat cross-entropy is a parameterized cross-entropy with the parameter α. Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy for α=1. The influence of this parameter on the final prediction results is studied. In this paper, the experiments are conducted on two datasets including in total 580 patients, of whom 434 suffered from head–neck cancers and 146 from lung cancers. The results show that Tsallis–Havrda–Charvat entropy can achieve better performance in terms of prediction accuracy with some values of α.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction

Brochet

Lapuyade‐Lahorgue

Huat

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Entropy is a measurable physical quality most usually linked with disorder, unpredictability, or uncertainty(E.-W. Huang et al, 2022).The smallest average encoding size per transmission with which a source can efficiently convey a message to a destination without losing any data is defined as entropy (Janik, 2019).The difference between two probability distributions for a given random variable or set of occurrences is measured by cross-entropy. (Brochet, Lapuyade-Lahorgue, Bougleux, Salaün, & Ruan, 2021;Gordon-Rodriguez, Loaiza-Ganem, Pleiss, & Cunningham, 2020;Pacheco, Ali, & Trappenberg, 2019;Ruby & Yendapalli, 2020;Z. Z. Wang & Goh, 2022).…”

Section: Overfitting and Underfitting In ML Modelsmentioning

confidence: 99%

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Upreti¹

2022

Preprint

View full text Add to dashboard Cite

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

show abstract

“…As a loss function, cross-entropy is extensively employed in ML [73] . Each example has a known class label with a probability of 1.0, whereas all other labels have a probability of 0.0 in classification [74] . In this case, the model determines the probability that a given example corresponds to each class label [75] .…”

Section: Cross-entropy and Cross-validationmentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

View full text Add to dashboard Cite

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

show abstract

Deep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy

Cited by 10 publications

References 30 publications

A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction

A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Machine learning application in GIS and remote sensing: An overview

Contact Info

Product

Resources

About