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Article Dans Une Revue Methods Année : 2022

PENet: Prior evidence deep neural network for bladder cancer staging

Xiaoqian Zhou
  • Fonction : Auteur
  • PersonId : 1121866
Xiaodong Yue
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Zhikang Xu
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  • PersonId : 1121867
Yufei Chen
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  • PersonId : 1121868

Résumé

Bladder cancer is a heterogeneous, complicated, and widespread illness with high rates of morbidity, death, and expense if not treated adequately. The accurate and exact stage of bladder cancer is fundamental for treatment choices and prognostic forecasts, as indicated by convincing evidence from randomized trials. The extraordinary capability of Deep Convolutional Neural Networks (DCNNs) to extract features is one of the primary advantages offered by these types of networks. DCNNs work well in numerous real clinical medical applications as it demands costly large-scale data annotation. However, a lack of background information hinders its effectiveness and interpretability. Clinicians identify the stage of a tumor by evaluating whether the tumor is muscle-invasive, as shown in images by the tumor's infiltration of the bladder wall. Incorporating this clinical knowledge in DCNN has the ability to enhance the performance of bladder cancer staging and bring the prediction into accordance with medical principles. Therefore, we introduce PENet, innovative prior evidence deep neural network, for classifying MR images of bladder cancer staging in line with clinical knowledge. To do this, first, the degree to which the tumor has penetrated into the bladder wall is measured to get prior distribution parameters of class probability called
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Dates et versions

hal-03773273 , version 1 (09-09-2022)

Identifiants

Citer

Xiaoqian Zhou, Xiaodong Yue, Zhikang Xu, Thierry Denoeux, Yufei Chen. PENet: Prior evidence deep neural network for bladder cancer staging. Methods, 2022, 207, pp.20-28. ⟨10.1016/j.ymeth.2022.08.010⟩. ⟨hal-03773273⟩
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