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Simple and robust depth-wise cascaded network for polyp segmentation
journal contribution
posted on 2023-03-14, 00:12 authored by TM Khan, M Arsalan, Imran RazzakImran Razzak, E MeijeringThe segmentation of the polyp region in colonoscopy images is considered difficult due to size, texture, and color variation. To segment polyps successfully, models based on convolutional neural networks (CNN), transformers, and their combinations have been developed. However, these methods are limited in that they can only model the local appearance of polyps or lack multi-level feature representation for spatial dependency in the decoding process. In this paper, we propose a simple, efficient yet powerful polyp segmentation framework that unifies the network with a multiscale cascaded path. The proposed MMS-Net utilizes multiscale and multipath convolutional operations in conjunction with multiple deep feature aggregation. The overall dense empowered features are sufficient for pixel-by-pixel detection of the polyp region. Extensive experiments on two popular benchmark datasets for polyp segmentation (Kvasir and CVC-Clinic DB) and two datasets of other medical applications (DRIVE and MC) are presented. The results show that our MMS-Net performs comparably to or better than other state-of-the-art methods despite having two or even three orders of magnitude fewer trainable parameters.
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Journal
Engineering Applications of Artificial IntelligenceVolume
121Article number
106023Pagination
1-10Location
Amsterdam, The NetherlandsPublisher DOI
ISSN
0952-1976Language
enPublication classification
C1.1 Refereed article in a scholarly journalPublisher
ElsevierUsage metrics
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