Lymphoma segmentation from 3D PET-CT images using a deep evidential
network

Lymphoma segmentation from 3D PET-CT images using a deep evidential network

31 January 2022

Thierry Denoeux

Papers citing "Lymphoma segmentation from 3D PET-CT images using a deep evidential network"

10 / 10 papers shown

Title
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes Aghiles Kebaili J. Lapuyade-Lahorgue Pierre Vera S. Ruan MedIm 31 0 0 17 Jun 2024
Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Network Xin Tie Muheon Shin Changhee Lee Scott B. Perlman Zachary Huemann ... K. McCarten Adina L. Alazraki Junjie Hu Steve Y. Cho Tyler J. Bradshaw 24 1 0 12 Apr 2024
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods Ling Huang S. Ruan Yucheng Xing Mengling Feng 33 20 0 09 Oct 2023
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation Ling Huang S. Ruan P. Decazes Thierry Denoeux EDL MedIm 22 1 0 12 Sep 2023
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty K. Zou Yidi Chen Ling Huang Xuedong Yuan Xiaojing Shen Meng Wang Rick Siow Mong Goh Yong-Jin Liu H. Fu UQCV 23 4 0 01 Jan 2023
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis Benjamin Lambert Florence Forbes A. Tucholka Senan Doyle Harmonie Dehaene M. Dojat 24 76 0 05 Oct 2022
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers Thierry Denoeux UQCV 11 12 0 01 Aug 2022
Evidence fusion with contextual discounting for multi-modality medical image segmentation Ling Huang Thierry Denoeux P. Vera S. Ruan MedIm 22 27 0 23 Jun 2022
Application of belief functions to medical image segmentation: A review Ling Huang S. Ruan Thierry Denoeux EDL MedIm 27 30 0 03 May 2022
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,134 0 06 Jun 2015