ArticleHealthcare technology letters2024
PlutoNet: An efficient polyp segmentation network with modified partial decoder and decoder consistency training.
Article in Healthcare technology letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- PlutoNet: An efficient polyp segmentation network with modified partial decoder and decoder consistency training.Healthcare technology letters · 2024Article
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2 authors.
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Abstract
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, PlutoNet is proposed 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, a novel
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