TY  -  
T1  - Test-time augmentation for deep learning-based cell segmentation on microscopy images 
SN  -  /  
UR  - http://hdl.handle.net/10138/318953 
T3  -  
A1  - Moshkov, Nikita; Mathe, Botond; Kertesz-Farkas, Attila; Hollandi, Reka; Horvath, Peter 
A2  -  
PB  -  
Y1  - 2020 
LA  - eng 
AB  - Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require a massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques. A less common way relies on test-time augmentation (TTA) which yields transformed versions of the ima... 
VO  -  
IS  -  
SP  -  
OP  -  
KW  - 1182 Biochemistry, cell and molecular biology 
N1  -   
PP  -  
ER  -