PlutoNet: An Efficient Polyp Segmentation Network with Modified Partial
Decoder and Decoder Consistency Training
- Tugberk Erol,
- Duygu Sarikaya
Abstract
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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.11 Nov 2024Submitted to Healthcare Technology Letters 12 Nov 2024Submission Checks Completed
12 Nov 2024Assigned to Editor
12 Nov 2024Reviewer(s) Assigned
22 Nov 2024Review(s) Completed, Editorial Evaluation Pending
25 Nov 2024Editorial Decision: Accept