Squeeze and multi‐context attention for polyp segmentation
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Squeeze and multi‐context attention for polyp segmentation. / Bhattacharya, Debayan; Eggert, Dennis; Betz, Christian; Schlaefer, Alexander.
in: INT J IMAG SYST TECH, Jahrgang 33, Nr. 1, 26.08.2022, S. 123-142.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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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 -