FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385383448> ?p ?o ?g. }

• W4385383448 endingPage "103520" @default.
• W4385383448 startingPage "103520" @default.
• W4385383448 abstract "Integration of whole slide imaging (WSI) and deep learning technology has led to significant improvements in the screening and diagnosis of cervical cancer. WSI enables the examination of all cells on a slide simultaneously and deep learning algorithms can accurately label them as cancerous or non-cancerous. Although many studies have investigated the application of deep learning for diagnosing various diseases, there is a lack of research focusing on the evolution, limitations, and gaps of intelligent algorithms in conjunction with WSI for cervical cancer. This paper provides a comprehensive overview of the state-of-the-art deep learning algorithms used for the timely and precise analysis of cervical WSI images. A total of 115 relevant papers were reviewed, and 37 were selected after screening with specific inclusion and exclusion criteria. Methodological aspects including deep learning techniques, data sources, architectures, and classification techniques employed by the selected studies were analyzed. The review presents the most popular techniques and current trends in deep learning-based cervical classification systems, and categorizes the evolution of the domain based on deep learning techniques, citing an in-depth analysis of various models developed over time. The paper advocates for the implementation of transfer supervised learning when utilizing deep learning models such as ResNet, VGG19, and EfficientNet, and builds a solid foundation for applying relevant techniques in different fields. Although some progress has been made in developing novel models for the diagnosis of cervical cancer, substantial work remains to be done in creating standardized benchmark databases of WSI images for the research community. This paper serves as a comprehensive guide for understanding the fundamental concepts, benefits, and challenges related to various deep learning models on WSI, including their application for cervical system classification. Additionally, it provides valuable insights into future research directions in this area." @default.
• W4385383448 created "2023-07-30" @default.
• W4385383448 creator A5089271238 @default.
• W4385383448 creator A5092567086 @default.
• W4385383448 date "2023-10-01" @default.
• W4385383448 modified "2023-09-25" @default.
• W4385383448 title "Recent Developments in Cervical Cancer Diagnosis using Deep Learning on Whole Slide Images: An Overview of Models, Techniques, Challenges and Future Directions" @default.
• W4385383448 cites W1977556410 @default.
• W4385383448 cites W1981938908 @default.
• W4385383448 cites W2089468765 @default.
• W4385383448 cites W2099741732 @default.
• W4385383448 cites W2113874502 @default.
• W4385383448 cites W2131255017 @default.
• W4385383448 cites W2139159280 @default.
• W4385383448 cites W2146655125 @default.
• W4385383448 cites W2151753845 @default.
• W4385383448 cites W2168130919 @default.
• W4385383448 cites W2256207883 @default.
• W4385383448 cites W2294157112 @default.
• W4385383448 cites W2346062110 @default.
• W4385383448 cites W2576404523 @default.
• W4385383448 cites W2734583741 @default.
• W4385383448 cites W2746791238 @default.
• W4385383448 cites W2794680598 @default.
• W4385383448 cites W2810207020 @default.
• W4385383448 cites W2891729389 @default.
• W4385383448 cites W2901312569 @default.
• W4385383448 cites W2934399013 @default.
• W4385383448 cites W2936573766 @default.
• W4385383448 cites W2943370629 @default.
• W4385383448 cites W2954888241 @default.
• W4385383448 cites W2955181859 @default.
• W4385383448 cites W2962642944 @default.
• W4385383448 cites W2974574331 @default.
• W4385383448 cites W2991430586 @default.
• W4385383448 cites W2993250673 @default.
• W4385383448 cites W3003607530 @default.
• W4385383448 cites W3004380884 @default.
• W4385383448 cites W3022086662 @default.
• W4385383448 cites W3033382446 @default.
• W4385383448 cites W3034124771 @default.
• W4385383448 cites W3034553879 @default.
• W4385383448 cites W3037308786 @default.
• W4385383448 cites W3086240734 @default.
• W4385383448 cites W3118232860 @default.
• W4385383448 cites W3121859091 @default.
• W4385383448 cites W3126485036 @default.
• W4385383448 cites W3130039502 @default.
• W4385383448 cites W3133063036 @default.
• W4385383448 cites W3134825748 @default.
• W4385383448 cites W3134866734 @default.
• W4385383448 cites W3156282278 @default.
• W4385383448 cites W3156423522 @default.
• W4385383448 cites W3156804249 @default.
• W4385383448 cites W3172375847 @default.
• W4385383448 cites W3181029726 @default.
• W4385383448 cites W3184433759 @default.
• W4385383448 cites W3191371758 @default.
• W4385383448 cites W3200847902 @default.
• W4385383448 cites W4211097225 @default.
• W4385383448 cites W4220845045 @default.
• W4385383448 cites W4221005924 @default.
• W4385383448 cites W4226066251 @default.
• W4385383448 cites W4237035446 @default.
• W4385383448 cites W4240035482 @default.
• W4385383448 cites W4244207479 @default.
• W4385383448 cites W4252440964 @default.
• W4385383448 cites W4295939052 @default.
• W4385383448 cites W4307943852 @default.
• W4385383448 cites W4308471057 @default.
• W4385383448 cites W4308868318 @default.
• W4385383448 cites W4313525579 @default.
• W4385383448 cites W4315630793 @default.
• W4385383448 cites W4323807484 @default.
• W4385383448 doi "https://doi.org/10.1016/j.micron.2023.103520" @default.
• W4385383448 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37556898" @default.
• W4385383448 hasPublicationYear "2023" @default.
• W4385383448 type Work @default.
• W4385383448 citedByCount "0" @default.
• W4385383448 crossrefType "journal-article" @default.
• W4385383448 hasAuthorship W4385383448A5089271238 @default.
• W4385383448 hasAuthorship W4385383448A5092567086 @default.
• W4385383448 hasConcept C108583219 @default.
• W4385383448 hasConcept C119857082 @default.
• W4385383448 hasConcept C121608353 @default.
• W4385383448 hasConcept C126322002 @default.
• W4385383448 hasConcept C13280743 @default.
• W4385383448 hasConcept C134306372 @default.
• W4385383448 hasConcept C150899416 @default.
• W4385383448 hasConcept C154945302 @default.
• W4385383448 hasConcept C185798385 @default.
• W4385383448 hasConcept C205649164 @default.
• W4385383448 hasConcept C2778220009 @default.
• W4385383448 hasConcept C33923547 @default.
• W4385383448 hasConcept C36503486 @default.
• W4385383448 hasConcept C41008148 @default.
• W4385383448 hasConcept C71924100 @default.
• W4385383448 hasConceptScore W4385383448C108583219 @default.

• next