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Journal Articles Neurocomputing Year : 2023

Semi-supervised multiple evidence fusion for brain tumor segmentation

Abstract

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.
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Dates and versions

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

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Cite

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⟩
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