8 citations · 12 across the 3 of their papers we have counts for
3 papers
Extracting temporal features into a spatial domain using autoencoders for sperm video analysis
Vajira Thambawita, Pål Halvorsen, Hugo Hammer +2
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. First, we use an aut…
Stacked dense optical flows and dropout layers to predict sperm motility and morphology
Vajira Thambawita, Pål Halvorsen, Hugo Hammer +2
In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and…
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Steven A. Hicks, Jorunn M. Andersen, Oliwia Witczak +5
Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human repr…