Matches in SemOpenAlex for { <https://semopenalex.org/work/W4323030615> ?p ?o ?g. }
- W4323030615 endingPage "87" @default.
- W4323030615 startingPage "73" @default.
- W4323030615 abstract "The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic substances. Viruses, obesity, alcohol use, and other factors can damage the liver and cause liver disease. The diagnosis of liver disease used to depend on the clinical experience of doctors, which made it subjective, difficult, and time-consuming. Deep learning has made breakthroughs in various fields; thus, there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and treatment. In this paper, we provide an overview of deep learning in liver research using 139 papers from the last 5 years. We also show the relationship between data modalities, liver topics, and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic, in addition to the relations and trends between these methods. Finally, we discuss the challenges of and expectations for deep learning in liver research." @default.
- W4323030615 created "2023-03-04" @default.
- W4323030615 creator A5000545817 @default.
- W4323030615 creator A5032223758 @default.
- W4323030615 creator A5060008686 @default.
- W4323030615 creator A5072629441 @default.
- W4323030615 date "2023-03-01" @default.
- W4323030615 modified "2023-10-02" @default.
- W4323030615 title "When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects" @default.
- W4323030615 cites W1856151485 @default.
- W4323030615 cites W2002349285 @default.
- W4323030615 cites W2007440415 @default.
- W4323030615 cites W2008979634 @default.
- W4323030615 cites W2096108416 @default.
- W4323030615 cites W2120397203 @default.
- W4323030615 cites W2124273275 @default.
- W4323030615 cites W2128898977 @default.
- W4323030615 cites W2130672074 @default.
- W4323030615 cites W2343738941 @default.
- W4323030615 cites W2592905743 @default.
- W4323030615 cites W2593539364 @default.
- W4323030615 cites W2618530766 @default.
- W4323030615 cites W2618718534 @default.
- W4323030615 cites W2761031851 @default.
- W4323030615 cites W2761701131 @default.
- W4323030615 cites W2765571304 @default.
- W4323030615 cites W2765811365 @default.
- W4323030615 cites W2794022343 @default.
- W4323030615 cites W2794287273 @default.
- W4323030615 cites W2809254203 @default.
- W4323030615 cites W2884281122 @default.
- W4323030615 cites W2884405614 @default.
- W4323030615 cites W2885478230 @default.
- W4323030615 cites W2888011474 @default.
- W4323030615 cites W2904572485 @default.
- W4323030615 cites W2909771648 @default.
- W4323030615 cites W2913347479 @default.
- W4323030615 cites W2913637767 @default.
- W4323030615 cites W2914473038 @default.
- W4323030615 cites W2916686134 @default.
- W4323030615 cites W2921682280 @default.
- W4323030615 cites W2923051440 @default.
- W4323030615 cites W2940773812 @default.
- W4323030615 cites W2941555836 @default.
- W4323030615 cites W2943557283 @default.
- W4323030615 cites W2944851425 @default.
- W4323030615 cites W2944918936 @default.
- W4323030615 cites W2959044096 @default.
- W4323030615 cites W2962949934 @default.
- W4323030615 cites W2964227007 @default.
- W4323030615 cites W2965682404 @default.
- W4323030615 cites W2967788260 @default.
- W4323030615 cites W2972256375 @default.
- W4323030615 cites W2981994674 @default.
- W4323030615 cites W2984306354 @default.
- W4323030615 cites W2985331920 @default.
- W4323030615 cites W2986785750 @default.
- W4323030615 cites W2990354819 @default.
- W4323030615 cites W2991139962 @default.
- W4323030615 cites W2993206503 @default.
- W4323030615 cites W3002569343 @default.
- W4323030615 cites W3002876351 @default.
- W4323030615 cites W3004510371 @default.
- W4323030615 cites W3007926128 @default.
- W4323030615 cites W3018177167 @default.
- W4323030615 cites W3041133507 @default.
- W4323030615 cites W3048012830 @default.
- W4323030615 cites W3048224461 @default.
- W4323030615 cites W3082662855 @default.
- W4323030615 cites W3088226460 @default.
- W4323030615 cites W3092842991 @default.
- W4323030615 cites W3096502673 @default.
- W4323030615 cites W3096831136 @default.
- W4323030615 cites W3109874103 @default.
- W4323030615 cites W3110274389 @default.
- W4323030615 cites W3116630564 @default.
- W4323030615 cites W3117796717 @default.
- W4323030615 cites W3117954678 @default.
- W4323030615 cites W3119778913 @default.
- W4323030615 cites W3121747126 @default.
- W4323030615 cites W3125726435 @default.
- W4323030615 cites W3125868735 @default.
- W4323030615 cites W3126512887 @default.
- W4323030615 cites W3127745304 @default.
- W4323030615 cites W3133325202 @default.
- W4323030615 cites W3135058056 @default.
- W4323030615 cites W3137734413 @default.
- W4323030615 cites W3153830729 @default.
- W4323030615 cites W3162790191 @default.
- W4323030615 cites W3165800762 @default.
- W4323030615 cites W3166758207 @default.
- W4323030615 cites W3168601957 @default.
- W4323030615 cites W3170210531 @default.
- W4323030615 cites W3176616269 @default.
- W4323030615 cites W3178880621 @default.
- W4323030615 cites W3191417184 @default.
- W4323030615 cites W3206770282 @default.
- W4323030615 cites W3210230163 @default.