Semi-supervised multiple evidence fusion for brain tumor segmentation - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes Access content directly
Journal Articles Neurocomputing Year : 2023

Semi-supervised multiple evidence fusion for brain tumor segmentation


The performance of deep learning-based methods depends mainly on the availability of largescale labeled learning data. However, obtaining precisely annotated examples is challenging in the medical domain. Although some semi-supervised deep learning methods have been proposed to train models with fewer labels, only a few studies have focused on the uncertainty caused by the low quality of the images and the lack of annotations. This paper addresses the above issues using Dempster-Shafer theory and deep learning: 1) a semi-supervised learning algorithm is proposed based on an image transformation strategy; 2) a probabilistic deep neural network and an evidential neural network are used in parallel to provide two sources of segmentation evidence; 3) Dempster's rule is used to combine the two pieces of evidence and reach a final segmentation result. Results from a series of experiments on the BraTS2019 brain tumor dataset show that our framework achieves promising results when only some training data are labeled.
Fichier principal
Vignette du fichier
Neurocomputing2022_Semi_v3.pdf (2.09 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04033265 , version 1 (17-03-2023)



Ling Huang, Su Ruan, Thierry Denœux. Semi-supervised multiple evidence fusion for brain tumor segmentation. Neurocomputing, 2023, 535, pp.40-52. ⟨10.1016/j.neucom.2023.02.047⟩. ⟨hal-04033265⟩
23 View
112 Download



Gmail Facebook X LinkedIn More