  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.
