PolypNextLSTM: a lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM.

ABSTRACT:

Purpose

Commonly employed in polyp segmentation, single-image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leverages video-based deep learning, harnessing temporal information for superior segmentation performance with least parameter overhead, making it possibly suitable for edge devices.

Methods

PolypNextLSTM employs a UNet-like structure with ConvNext-Tiny as its backbone, strategically omitting the last two layers to reduce parameter overhead. Our temporal fusion module, a Convolutional Long Short Term Memory (ConvLSTM), effectively exploits temporal features. Our primary novelty lies in PolypNextLSTM, which stands out as the leanest i

SUBMITTER: Bhattacharya D 

PROVIDER: S-EPMC11442634 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature