Belief function-based semi-supervised learning for brain tumor segmentation
This work addresses the challenge of unreliable segmentation due to labeling uncertainty and data scarcity in medical imaging, which is incremental as it builds on existing semi-supervised and evidential methods.
The paper tackles the problem of uncertain boundaries and scarce annotated data in brain tumor segmentation by introducing a new evidential neural network with an information fusion strategy and semi-supervised learning, achieving better performance than state-of-the-art methods.
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.