GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation

• URL: http://arxiv.org/abs/2111.10614v1
• Date: Sat, 20 Nov 2021 15:41:59 GMT
• Title: GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation
• Authors: Abhishek Srivastava, Sukalpa Chanda, Debesh Jha, Umapada Pal, and Sharib Ali
• Abstract summary: Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy.
• Score: 12.086664133486144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop and require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations while performing multi-scale fusion operations for all resolution scales. To further leverage scale information, we design cross multi-scale attention (CMSA) and multi-scale feature selection (MSFS) modules within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of the network. Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34% and 10.31% on unseen CVC-ClinicDB and unseen Kvasir-SEG, in terms of dice coefficient.

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