  Segmentation of tumors in brain MRI images is a challenging task, where most
recent methods demand large volumes of data with pixel-level annotations, which
are generally costly to obtain. In contrast, image-level annotations, where
only the presence of lesion is marked, are generally cheap, generated in far
larger volumes compared to pixel-level labels, and contain less labeling noise.
In the context of brain tumor segmentation, both pixel-level and image-level
annotations are commonly available; thus, a natural question arises whether a
segmentation procedure could take advantage of both. In the present work we: 1)
propose a learning-based framework that allows simultaneous usage of both
pixel- and image-level annotations in MRI images to learn a segmentation model
for brain tumor; 2) study the influence of comparative amounts of pixel- and
image-level annotations on the quality of brain tumor segmentation; 3) compare
our approach to the traditional fully-supervised approach and show that the
performance of our method in terms of segmentation quality may be competitive.
