Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285176390> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4285176390 endingPage "150" @default.
- W4285176390 startingPage "133" @default.
- W4285176390 abstract "Deep learning and the field of artificial intelligence have been buzzwords for years with their popularity continually growing. Deep learning is an area of machine learning that was inspired by the way our brains work. Thus, by using a mesh of layers that we refer to as neural networks, step by step each layer of the process takes in different information which will then output results. Deep learning has slowly shown itself to be a real powerhouse in the machine learning industry, particularly in its use of “Big Data.” The healthcare industry is primed for this sort of advanced machine learning application. The vast amount of data that could be collected paired with the importance of getting efficient and correct results shows a great path for deep learning to be introduced into the healthcare sector. Of course, there are many ways in which deep learning could be applied to the healthcare industry. However, one that is quite fascinating, is the use of Deep learning for image analysis, particularly in the field of oncology. Deep learning could be employed from diagnosis and prognosis to real-time surveillance of the analysis to the creation of treatment plans designed specifically for each of the individual patients. All of this would be made possible from the data collected and processed through deep learning image analysis models. In this chapter, we will analyze some of the most common deep learning algorithms used for this process, some of the ground-breaking work that has already been used in this field and discusses what next steps will lead to a future with deep learning image analysis in the study of oncology." @default.
- W4285176390 created "2022-07-14" @default.
- W4285176390 creator A5009691111 @default.
- W4285176390 creator A5041541232 @default.
- W4285176390 date "2022-01-01" @default.
- W4285176390 modified "2023-09-25" @default.
- W4285176390 title "The use of deep learning in image analysis for the study of oncology" @default.
- W4285176390 cites W2134584543 @default.
- W4285176390 cites W2190051895 @default.
- W4285176390 cites W2476370993 @default.
- W4285176390 cites W2751924564 @default.
- W4285176390 cites W2792411327 @default.
- W4285176390 cites W2794803511 @default.
- W4285176390 cites W2904319976 @default.
- W4285176390 cites W2944757226 @default.
- W4285176390 cites W2945263066 @default.
- W4285176390 cites W2995745991 @default.
- W4285176390 cites W3006463225 @default.
- W4285176390 cites W3010928607 @default.
- W4285176390 cites W3013851350 @default.
- W4285176390 cites W3101285135 @default.
- W4285176390 doi "https://doi.org/10.1016/b978-0-32-385845-8.00011-3" @default.
- W4285176390 hasPublicationYear "2022" @default.
- W4285176390 type Work @default.
- W4285176390 citedByCount "1" @default.
- W4285176390 countsByYear W42851763902022 @default.
- W4285176390 crossrefType "book-chapter" @default.
- W4285176390 hasAuthorship W4285176390A5009691111 @default.
- W4285176390 hasAuthorship W4285176390A5041541232 @default.
- W4285176390 hasConcept C108583219 @default.
- W4285176390 hasConcept C111919701 @default.
- W4285176390 hasConcept C119857082 @default.
- W4285176390 hasConcept C124101348 @default.
- W4285176390 hasConcept C154945302 @default.
- W4285176390 hasConcept C15744967 @default.
- W4285176390 hasConcept C202444582 @default.
- W4285176390 hasConcept C2522767166 @default.
- W4285176390 hasConcept C2780586970 @default.
- W4285176390 hasConcept C33923547 @default.
- W4285176390 hasConcept C41008148 @default.
- W4285176390 hasConcept C50644808 @default.
- W4285176390 hasConcept C75684735 @default.
- W4285176390 hasConcept C77805123 @default.
- W4285176390 hasConcept C9652623 @default.
- W4285176390 hasConcept C98045186 @default.
- W4285176390 hasConceptScore W4285176390C108583219 @default.
- W4285176390 hasConceptScore W4285176390C111919701 @default.
- W4285176390 hasConceptScore W4285176390C119857082 @default.
- W4285176390 hasConceptScore W4285176390C124101348 @default.
- W4285176390 hasConceptScore W4285176390C154945302 @default.
- W4285176390 hasConceptScore W4285176390C15744967 @default.
- W4285176390 hasConceptScore W4285176390C202444582 @default.
- W4285176390 hasConceptScore W4285176390C2522767166 @default.
- W4285176390 hasConceptScore W4285176390C2780586970 @default.
- W4285176390 hasConceptScore W4285176390C33923547 @default.
- W4285176390 hasConceptScore W4285176390C41008148 @default.
- W4285176390 hasConceptScore W4285176390C50644808 @default.
- W4285176390 hasConceptScore W4285176390C75684735 @default.
- W4285176390 hasConceptScore W4285176390C77805123 @default.
- W4285176390 hasConceptScore W4285176390C9652623 @default.
- W4285176390 hasConceptScore W4285176390C98045186 @default.
- W4285176390 hasLocation W42851763901 @default.
- W4285176390 hasOpenAccess W4285176390 @default.
- W4285176390 hasPrimaryLocation W42851763901 @default.
- W4285176390 hasRelatedWork W3014300295 @default.
- W4285176390 hasRelatedWork W3166850502 @default.
- W4285176390 hasRelatedWork W3189515467 @default.
- W4285176390 hasRelatedWork W4223943233 @default.
- W4285176390 hasRelatedWork W4225161397 @default.
- W4285176390 hasRelatedWork W4309045103 @default.
- W4285176390 hasRelatedWork W4312200629 @default.
- W4285176390 hasRelatedWork W4312831135 @default.
- W4285176390 hasRelatedWork W4360585206 @default.
- W4285176390 hasRelatedWork W4364306694 @default.
- W4285176390 isParatext "false" @default.
- W4285176390 isRetracted "false" @default.
- W4285176390 workType "book-chapter" @default.