Belief function-based semi-supervised learning for brain tumor segmentation

• URL: http://arxiv.org/abs/2102.00097v1
• Date: Fri, 29 Jan 2021 22:39:16 GMT
• Title: Belief function-based semi-supervised learning for brain tumor segmentation
• Authors: Ling Huang, Su Ruan, Thierry Denoeux
• Abstract summary: Deep learning makes it possible to detect and segment a lesion field using annotated data. However, obtaining precisely annotated data is very challenging in the medical domain. In this paper, we address the uncertain boundary problem by a new evidential neural network with an information fusion strategy.
• Score: 23.21410263735263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise segmentation of a lesion area is important for optimizing its treatment. Deep learning makes it possible to detect and segment a lesion field using annotated data. However, obtaining precisely annotated data is very challenging in the medical domain. Moreover, labeling uncertainty and imprecision make segmentation results unreliable. In this paper, we address the uncertain boundary problem by a new evidential neural network with an information fusion strategy, and the scarcity of annotated data by semi-supervised learning. Experimental results show that our proposal has better performance than state-of-the-art methods.

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