Results 91 to 100 of about 44,134 (166)

Uncertainty-weighted semi-supervised learning with dynamic entropy masking and Bhattacharyya-regularized loss. [PDF]

Sci Rep
Ghazal MT, Tanha J, Shahi N, Roshan S.
europepmc   +1 more source

Srnc: semi-supervised learning for robust novel cell-type identification in single cell RNA sequencing data. [PDF]

BMC Bioinformatics
Nguyen TV, Do VH, Nguyen VL.
europepmc   +1 more source

Balance and fall-risk assessment in older adults using wearable plantar pressure and semi-supervised learning. [PDF]

Front Bioeng Biotechnol
Ou J, Chen F, Liao C, Song Z, Liu L, Song X, Bi W, Wang L, Shu L, Chen Z.   +9 more
europepmc   +1 more source

Motion feature extraction based on semi-supervised learning and long short-term memory network in digital dance. [PDF]

Front Neurorobot
Yang X, Sun H, Lyu Y, Sun Y.
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Subclass-Aware Contrastive Semi-Supervised Learning for Inflammatory Bowel Disease Classification from Colonoscopy Images. [PDF]

Bioengineering (Basel)
Lin K, Ruan G, Zou X, Nian Y, Wei Y, Zheng G.   +5 more
europepmc   +1 more source

Domain-adaptive semi-supervised learning for efficient rare pathological lesion detection with minimal annotation. [PDF]

NPJ Digit Med
Matsui I, Matsumoto A, Imai A, Okushima H, Niioka H, Abe M, Tamai N, Nagasu H, Kanda E, Uchino E, Sofue T, Imasawa T, Yano Y, Kinashi H, Miyoshi KI, Harada T, Nagasawa Y, Fujimoto K, Kurokawa Y, Kato S, Kaseda R, Koizumi M, Kusunoki Y, Ohya M, Kawazoe Y, Abe H, Matsukuma Y, Kosugi T, Ueda Y, Fujii N, Takeji M, Suzuki A, Nagatoya K, Oka K, Ando Y, Izumi M, Komiya T, Tsukamoto T, Mimura I, Kuragano T, Nakano T, Tsuruya K, Ito Y, Minamino T, Yamaguchi O, Yamamoto S, Komaba H, Furuichi K, Fukami K, Araki SI, Masaki T, Tsuboi N, Yokoyama H, Shimizu A, Ushiku T, Maruyama S, Yanagita M, Nangaku M, Yamamoto R, Inoue K, Isaka Y.   +60 more
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River extraction from high-resolution remote sensing images based on non-uniform sampling and semi-supervised learning. [PDF]

Sci Rep
Wang K, Han L, Li L.
europepmc   +1 more source

Semi-Supervised Learning for Predicting Multiple Sclerosis. [PDF]

J Pers Med
Kotsiantis S, Melagraki G, Verykios V, Sakagianni A, Matsoukas J.   +4 more
europepmc   +1 more source

Advanced fault diagnosis in milling cutting tools using vision transformers with semi-supervised learning and uncertainty quantification. [PDF]

Sci Rep
Siddique MF, Umar M, Ahmad W, Kim JM.
europepmc   +1 more source

Federated Semi-Supervised Learning with Uniform Random and Lattice-Based Client Sampling. [PDF]

Entropy (Basel)
Zhang M, Yang F.
europepmc   +1 more source