Decision-making support system for diagnosis of oncopathologies by histological images
Tài liệu tham khảo
Badrinarayanan, 2017, SegNet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans Pattern Anal Mach Intel., 39, 2481, 10.1109/TPAMI.2016.2644615
Avramenko, 2020, Operative recognition of standard signal types, Радіоелектроніка, інформатика, управління, 2. C, 75
Luo, 2016, Use cases for digital pathology, 5
Gerrit van den Burg, 2016, GenSVM: a generalized multiclass support vector machine, J Mach Learn Res., 17, 1
Beckwith, 2016, 87
Moskalenko, 2017, Extreme algorithm of the system for recognition of objects on the terrain with optimization parameter feature extraction, Radio Electronics, Computer Science, Control, 2, 38
Xu, 2013, 284
Rozhkova, 2020, Neural network-based segmentation model for breast cancer X-ray screening, Sechenov Med J., 11, 4, 10.47093/2218-7332.2020.11.3.4-14
Ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, 234
Dovbysh, 2014, Information-extreme learning algorithm for a system of recognition of morphological images in diagnosing oncological pathologies, Cybernet Syst Anal., 50, 157, 10.1007/s10559-014-9603-y
Dovbysh, 2020, Information-extreme machine learning of on-board vehicle recognition system, Cybernet Syst Anal, 56, 534, 10.1007/s10559-020-00269-y
Naumenko, 2021, Information-extreme machine training of on-board recognition system with optimization of RGB-component digital images, Radioelect Comput Syst, 4, 59, 10.32620/reks.2021.4.05
Dovbysh, 2019, Estimation of informativeness of recognition signs at extreme information machine learning of knowledge control system, CEUR Workshop Proc, 2362, 143