1 code implementation • 8 Nov 2019 • Vajira Thambawita, Pål Halvorsen, Hugo Hammer, Michael Riegler, Trine B. Haugen
In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology-based on video recordings of human spermatozoa.
no code implementations • 8 Nov 2019 • Vajira Thambawita, Pål Halvorsen, Hugo Hammer, Michael Riegler, Trine B. Haugen
To solve this regression task of predicting motility and morphology, stacked dense optical flows and extracted original frames from sperm videos were used with the modified state of the art convolution neural networks.
1 code implementation • 3 Jul 2019 • Sidney Pontes-Filho, Anis Yazidi, Jianhua Zhang, Hugo Hammer, Gustavo B. M. Mello, Ioanna Sandvig, Gunnar Tufte, Stefano Nichele
The advantages of such implementation are its usage on specialized and optimized deep learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules.