Abstract

not-yet-known not-yet-known not-yet-known unknown 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 are computation and memory intensive, which can pose a problem with real-time applications. To address these problems, we propose PlutoNet for polyp segmentation which requires only 9 FLOPs and 2,626,537 parameters, less than 10% of the parameters required by its counterparts. With PlutoNet, we propose a novel 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 redundancy, and the auxiliary decoder which focuses on higher-level semantic features. We train the modified partial decoder and the auxiliary decoder with a combined loss to enforce consistency, which helps strengthen learned representations. We perform ablation studies and experiments which show that PlutoNet performs significantly better than the state-of-the-art models, particularly on unseen datasets.