CoInNet: A Convolution-Involution Network with a Novel Statistical Attention for Automatic Polyp Segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020726" target="_blank" >RIV/62690094:18450/23:50020726 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10266385" target="_blank" >https://ieeexplore.ieee.org/document/10266385</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TMI.2023.3320151" target="_blank" >10.1109/TMI.2023.3320151</a>
Alternative languages
Result language
angličtina
Original language name
CoInNet: A Convolution-Involution Network with a Novel Statistical Attention for Automatic Polyp Segmentation
Original language description
Polyps are very common abnormalities in human gastrointestinal regions. Their early diagnosis may help in reducing the risk of colorectal cancer. Vision-based computer-aided diagnostic systems automatically identify polyp regions to assist surgeons in their removal. Due to their varying shape, color, size, texture, and unclear boundaries, polyp segmentation in images is a challenging problem. Existing deep learning segmentation models mostly rely on convolutional neural networks that have certain limitations in learning the diversity in visual patterns at different spatial locations. Further, they fail to capture inter-feature dependencies. Vision transformer models have also been deployed for polyp segmentation due to their powerful global feature extraction capabilities. But they too are supplemented by convolution layers for learning contextual local information. In the present paper, a polyp segmentation model CoInNet is proposed with a novel feature extraction mechanism that leverages the strengths of convolution and involution operations and learns to highlight polyp regions in images by considering the relationship between different feature maps through a statistical feature attention unit. To further aid the network in learning polyp boundaries, an anomaly boundary approximation module is introduced that uses recursively fed feature fusion to refine segmentation results. It is indeed remarkable that even tiny-sized polyps with only 0.01% of an image area can be precisely segmented by CoInNet. It is crucial for clinical applications, as small polyps can be easily overlooked even in the manual examination due to the voluminous size of wireless capsule endoscopy videos. CoInNet outperforms thirteen state-of-the-art methods on five benchmark polyp segmentation datasets. IEEE
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Medical Imaging
ISSN
0278-0062
e-ISSN
1558-254X
Volume of the periodical
42
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
3987-4000
UT code for WoS article
001122030500041
EID of the result in the Scopus database
2-s2.0-85173076286