3.
Kyoto global consensus report on Helicobacter pylori gastritis
by Sugano, Kentaro
Gut, 2015, Vol.64 (9), p.1353-1367

9.
Terminology and classification of muscle injuries in sport: The Munich consensus statement
by Mueller-Wohlfahrt, Hans-Wilhelm
British journal of sports medicine, 2013, Vol.47 (6), p.342-350

10.
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning
by Coudray, Nicolas
Nature medicine, 2018, Vol.24 (10), p.1559-1567

11.
Clonal Analysis of the Microbiota of Severe Early Childhood Caries
by Kanasi, E
Caries research, 2010, Vol.44 (5), p.485-497

12.
International Photographic Classification and Grading System for Myopic Maculopathy
by Ohno-Matsui, Kyoko
American journal of ophthalmology, 2015, Vol.159 (5), p.877-883.e7

13.
Myoepithelial and epithelial–myoepithelial, mesenchymal and fibroepithelial breast lesions: updates from the WHO Classification of Tumours of the Breast 2012
by Tan, Puay Hoon
Journal of Clinical Pathology, 2013, Vol.66 (6), p.465-470

15.
EULAR/PReS endorsed consensus criteria for the classification of childhood vasculitides
by Ozen, S
Annals of the rheumatic diseases, 2006, Vol.65 (7), p.936-941

16.
EULAR/PRINTO/PRES criteria for Henoch–Schönlein purpura, childhood polyarteritis nodosa, childhood Wegener granulomatosis and childhood Takayasu arteritis: Ankara 2008. Part II: Fi...
by Ozen, Seza
Annals of the rheumatic diseases, 2010, Vol.69 (5), p.798-806

17.
Data scarcity, robustness and extreme multi-label classification
by Babbar, Rohit
Machine learning, 2019, Vol.108 (8-9), p.1329-1351

18.
Biology and Genetics of Prions Causing Neurodegeneration
by Prusiner, Stanley B
Annual review of genetics, 2013, Vol.47 (1), p.601-623

20.
Successive shifts in the microbial community of the surface mucus layer and tissues of the coral Acropora muricata under thermal stress
by Lee, Sonny T. M
FEMS microbiology ecology, 2015, Vol.91 (12), p.fiv142
