Titre :

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

Auteur(s) :

Brochet, Thibaud [Auteur]
Lapuyade-Lahorgue, Jérôme [Auteur]
Equipe Quantification en Imagerie Fonctionnelle [QuantIF-LITIS]
Huat, Alexandre [Auteur]
Thureau, Sébastien [Auteur]
Equipe Quantification en Imagerie Fonctionnelle [QuantIF-LITIS]
Pasquier, David [Auteur]
Gardin, Isabelle [Auteur]
Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen [CLCC Henri Becquerel]
Equipe Quantification en Imagerie Fonctionnelle [QuantIF-LITIS]
Modzelewski, Romain [Auteur]
Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen [CLCC Henri Becquerel]
Equipe Quantification en Imagerie Fonctionnelle [QuantIF-LITIS]
Gibon, David [Auteur]
Thariat, Juliette [Auteur]
Grégoire, Vincent [Auteur]
Centre Léon Bérard [Lyon]
Vera, Pierre [Auteur]
Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen [CLCC Henri Becquerel]
Equipe Quantification en Imagerie Fonctionnelle [QuantIF-LITIS]
Ruan, Su [Auteur]
Equipe Quantification en Imagerie Fonctionnelle [QuantIF-LITIS]

Titre de la revue :

Entropy

Pagination :

436

Éditeur :

MDPI

Date de publication :

2022-04

ISSN :

1099-4300

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL