  Deep learning models are used to minimize the number of polyps that goes
unnoticed by the experts and to accurately segment the detected polyps during
interventions. Although state-of-the-art models are proposed, it remains a
challenge to define representations that are able to generalize well and that
mediate between capturing low-level features and higher-level semantic details
without being redundant. Another challenge with these models is that they
require too many parameters, which can pose a problem with real-time
applications. To address these problems, we propose PlutoNet for polyp
segmentation which requires only 2,626,537 parameters, less than 10\% of the
parameters required by its counterparts. With PlutoNet, we propose a novel
\emph{decoder consistency training} approach that consists of a shared encoder,
the modified partial decoder which is a combination of the partial decoder and
full-scale connections that capture salient features at different scales
without being redundant, and the auxiliary decoder which focuses on
higher-level relevant semantic features. We train the modified partial decoder
and the auxiliary decoder with a combined loss to enforce consistency, which
helps improve the encoders representations. This way we are able to reduce
uncertainty and false positive rates. We perform ablation studies and extensive
experiments which show that PlutoNet performs significantly better than the
state-of-the-art models, particularly on unseen datasets and datasets across
different domains.
