Squeeze and multi‐context attention for polyp segmentation

TY - JOUR

T1 - Squeeze and multi‐context attention for polyp segmentation

AU - Bhattacharya, Debayan

AU - Eggert, Dennis

AU - Betz, Christian

AU - Schlaefer, Alexander

PY - 2022/8/26

Y1 - 2022/8/26

N2 - Artificial Intelligence-based Computer Aided Diagnostics (AI-CADx) have been proposed to help physicians in reducing misdetection of polyps in colonoscopy examination. The heterogeneity of a polyp's appearance makes detection challenging for physicians and AI-CADx. Towards building better AI-CADx, we propose an attention module called Squeeze and Multi-Context Attention (SMCA) that re-calibrates a feature map by providing channel and spatial attention by taking into consideration highly activated features and context of the features at multiple receptive fields simultaneously. We test the effectiveness of SMCA by incorporating it into the encoder of five popular segmentation models. We use five public datasets and construct intra-dataset and inter-dataset test sets to evaluate the generalizing capability of models with SMCA. Our intra-dataset evaluation shows that U-Net with SMCA and without SMCA has a precision of 0.86 ± 0.01 and 0.76 ± 0.02 respectively on CVC-ClinicDB. Our inter-dataset evaluation reveals that U-Net with SMCA and without SMCA has a precision of 0.62 ± 0.01 and 0.55 ± 0.09 respectively when trained on Kvasir-SEG and tested on CVC-ColonDB. Similar results are observed using other segmentation models and other public datasets. In conclusion, we demonstrate that incorporating SMCA into the segmentation models leads to an increase in generalizing capability of the segmentation models.

AB - Artificial Intelligence-based Computer Aided Diagnostics (AI-CADx) have been proposed to help physicians in reducing misdetection of polyps in colonoscopy examination. The heterogeneity of a polyp's appearance makes detection challenging for physicians and AI-CADx. Towards building better AI-CADx, we propose an attention module called Squeeze and Multi-Context Attention (SMCA) that re-calibrates a feature map by providing channel and spatial attention by taking into consideration highly activated features and context of the features at multiple receptive fields simultaneously. We test the effectiveness of SMCA by incorporating it into the encoder of five popular segmentation models. We use five public datasets and construct intra-dataset and inter-dataset test sets to evaluate the generalizing capability of models with SMCA. Our intra-dataset evaluation shows that U-Net with SMCA and without SMCA has a precision of 0.86 ± 0.01 and 0.76 ± 0.02 respectively on CVC-ClinicDB. Our inter-dataset evaluation reveals that U-Net with SMCA and without SMCA has a precision of 0.62 ± 0.01 and 0.55 ± 0.09 respectively when trained on Kvasir-SEG and tested on CVC-ColonDB. Similar results are observed using other segmentation models and other public datasets. In conclusion, we demonstrate that incorporating SMCA into the segmentation models leads to an increase in generalizing capability of the segmentation models.

U2 - 10.1002/ima.22795

DO - 10.1002/ima.22795

M3 - SCORING: Journal article

VL - 33

SP - 123

EP - 142

JO - INT J IMAG SYST TECH

JF - INT J IMAG SYST TECH

SN - 0899-9457

IS - 1

ER -