Deep Learning for Segmentation of Polyps for Early Prediction of Colorectal Cancer: A Prosperous Direction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning for Segmentation of Polyps for Early Prediction of Colorectal Cancer: A Prosperous Direction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep Learning for Segmentation of Polyps for Early Prediction of Colorectal Cancer: A Prosperous Direction

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Networks and Systems

ISBN

978-981-9926-79-4

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

8

Strana od-do

415-422

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Singapore

Místo konání akce

Ropar

Datum konání akce

19. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—