  Deep segmentation neural networks require large training datasets with
pixel-wise segmentations, which are expensive to obtain in practice. Mixed
supervision could mitigate this difficulty, with a small fraction of the data
containing complete pixel-wise annotations, while the rest being less
supervised, e.g., only a handful of pixels are labeled. In this work, we
propose a dual-branch architecture, where the upper branch (teacher) receives
strong annotations, while the bottom one (student) is driven by limited
supervision and guided by the upper branch. In conjunction with a standard
cross-entropy over the labeled pixels, our novel formulation integrates two
important terms: (i) a Shannon entropy loss defined over the less-supervised
images, which encourages confident student predictions at the bottom branch;
and (ii) a Kullback-Leibler (KL) divergence, which transfers the knowledge from
the predictions generated by the strongly supervised branch to the
less-supervised branch, and guides the entropy (student-confidence) term to
avoid trivial solutions. Very interestingly, we show that the synergy between
the entropy and KL divergence yields substantial improvements in performances.
Furthermore, we discuss an interesting link between Shannon-entropy
minimization and standard pseudo-mask generation and argue that the former
should be preferred over the latter for leveraging information from unlabeled
pixels. Through a series of quantitative and qualitative experiments, we show
the effectiveness of the proposed formulation in segmenting the left-ventricle
endocardium in MRI images. We demonstrate that our method significantly
outperforms other strategies to tackle semantic segmentation within a
mixed-supervision framework. More interestingly, and in line with recent
observations in classification, we show that the branch trained with reduced
supervision largely outperforms the teacher.
