Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226405060> ?p ?o ?g. }
- W4226405060 endingPage "39068" @default.
- W4226405060 startingPage "39045" @default.
- W4226405060 abstract "Recently, the rapid advancements in Deep Learning and Computer Vision technologies have introduced a new and exciting era in the field of skin disease analysis. However, there are certain challenges in the roadmap towards developing such technologies for real-life applications that must be investigated. This study considers one of the key challenges in data acquisition and computation, viz. data scarcity. Data scarcity is a central problem in acquiring medical images and applying machine learning techniques to train Convolutional Neural Networks for disease diagnosis. The main objective of this study is to explore the possible methods to deal with the data scarcity problem and to improve diagnosis with small datasets. The challenges in data acquisition for a few lamentably neglected skin conditions such as rosacea are an excellent instance to explore the possibilities of improving computer-aided skin disease diagnosis. With data scarcity in mind, the possible techniques explored and discussed include Generative Adversarial Networks, Meta-Learning, Few-Shot classification, and 3D face modelling. Furthermore, the existing studies are discussed based on skin conditions considered, data volume and implementation choices. Some future research directions are recommended." @default.
- W4226405060 created "2022-05-05" @default.
- W4226405060 creator A5002473893 @default.
- W4226405060 creator A5032061388 @default.
- W4226405060 creator A5060543167 @default.
- W4226405060 creator A5070582173 @default.
- W4226405060 date "2022-01-01" @default.
- W4226405060 modified "2023-10-13" @default.
- W4226405060 title "Skin Disease Analysis With Limited Data in Particular Rosacea: A Review and Recommended Framework" @default.
- W4226405060 cites W1972541034 @default.
- W4226405060 cites W1998451602 @default.
- W4226405060 cites W2002507614 @default.
- W4226405060 cites W2051521434 @default.
- W4226405060 cites W2068879084 @default.
- W4226405060 cites W2077411790 @default.
- W4226405060 cites W2078775693 @default.
- W4226405060 cites W2091798718 @default.
- W4226405060 cites W2105993323 @default.
- W4226405060 cites W2108598243 @default.
- W4226405060 cites W2112796928 @default.
- W4226405060 cites W2117539524 @default.
- W4226405060 cites W2152826766 @default.
- W4226405060 cites W2162935082 @default.
- W4226405060 cites W2164273268 @default.
- W4226405060 cites W2165698076 @default.
- W4226405060 cites W2180612164 @default.
- W4226405060 cites W2183341477 @default.
- W4226405060 cites W2194775991 @default.
- W4226405060 cites W2237250383 @default.
- W4226405060 cites W2310705318 @default.
- W4226405060 cites W2345010043 @default.
- W4226405060 cites W2519210008 @default.
- W4226405060 cites W2531409750 @default.
- W4226405060 cites W2581082771 @default.
- W4226405060 cites W2584229793 @default.
- W4226405060 cites W2585365015 @default.
- W4226405060 cites W2588978745 @default.
- W4226405060 cites W2591669284 @default.
- W4226405060 cites W2592124696 @default.
- W4226405060 cites W2599226450 @default.
- W4226405060 cites W2610332124 @default.
- W4226405060 cites W2617669016 @default.
- W4226405060 cites W2701556738 @default.
- W4226405060 cites W2731899572 @default.
- W4226405060 cites W2733972807 @default.
- W4226405060 cites W2742893201 @default.
- W4226405060 cites W2756770081 @default.
- W4226405060 cites W2772246530 @default.
- W4226405060 cites W2786147899 @default.
- W4226405060 cites W2786204509 @default.
- W4226405060 cites W2790847525 @default.
- W4226405060 cites W2792411327 @default.
- W4226405060 cites W2797527544 @default.
- W4226405060 cites W2798277818 @default.
- W4226405060 cites W2798291180 @default.
- W4226405060 cites W2804047627 @default.
- W4226405060 cites W2889871190 @default.
- W4226405060 cites W2895067176 @default.
- W4226405060 cites W2897228760 @default.
- W4226405060 cites W2901158775 @default.
- W4226405060 cites W2919115771 @default.
- W4226405060 cites W2936503027 @default.
- W4226405060 cites W2940790545 @default.
- W4226405060 cites W2945008339 @default.
- W4226405060 cites W2946122943 @default.
- W4226405060 cites W2947367580 @default.
- W4226405060 cites W2953396350 @default.
- W4226405060 cites W2954996726 @default.
- W4226405060 cites W2956391471 @default.
- W4226405060 cites W2962770929 @default.
- W4226405060 cites W2962974533 @default.
- W4226405060 cites W2963446712 @default.
- W4226405060 cites W2963800363 @default.
- W4226405060 cites W2963943197 @default.
- W4226405060 cites W2964105864 @default.
- W4226405060 cites W2968578647 @default.
- W4226405060 cites W2975651437 @default.
- W4226405060 cites W2979306089 @default.
- W4226405060 cites W2979830539 @default.
- W4226405060 cites W2979849298 @default.
- W4226405060 cites W2980113592 @default.
- W4226405060 cites W2989625509 @default.
- W4226405060 cites W2990026901 @default.
- W4226405060 cites W2994633389 @default.
- W4226405060 cites W2994949090 @default.
- W4226405060 cites W3014613513 @default.
- W4226405060 cites W3015903091 @default.
- W4226405060 cites W3018165536 @default.
- W4226405060 cites W3020995211 @default.
- W4226405060 cites W3020996329 @default.
- W4226405060 cites W3024256214 @default.
- W4226405060 cites W3024756171 @default.
- W4226405060 cites W3027969476 @default.
- W4226405060 cites W3029477994 @default.
- W4226405060 cites W3034192160 @default.
- W4226405060 cites W3034942609 @default.
- W4226405060 cites W3034988872 @default.
- W4226405060 cites W3035574324 @default.