Deep Learning for Segmentation of Polyps for Early Prediction of Colorectal Cancer: A Prosperous Direction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020688" target="_blank" >RIV/62690094:18450/23:50020688 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-981-99-2680-0_36" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-99-2680-0_36</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-99-2680-0_36" target="_blank" >10.1007/978-981-99-2680-0_36</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning for Segmentation of Polyps for Early Prediction of Colorectal Cancer: A Prosperous Direction
Original language description
Accurate segmentation of colorectal polyps is crucial for the early diagnosis of Colorectal Cancer (CRC). In clinical practice, the segmented polyp provides valuable diagnostic information to decide the degree of malignancy through optical biopsy. However, precise segmentation of polyps is very challenging as the appearance and morphology of polyps change in different stages of development in terms of size, color, and texture. In recent years, numerous deep learning (DL) techniques have been put forward by researchers across the globe for the polyp segmentation task. This study retrieved some significant deep learning-based polyp segmentation techniques through a systematic search strategy. The main purpose of this study is to provide an intuitive understanding of the techniques that have brought a major contribution to this field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Networks and Systems
ISBN
978-981-9926-79-4
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
8
Pages from-to
415-422
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Singapore
Event location
Ropar
Event date
Dec 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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