VISEM-Tracking, a human spermatozoa tracking dataset

1 code implementation • 6 Dec 2022 • Vajira Thambawita, Steven A. Hicks, Andrea M. Storås, Thu Nguyen, Jorunn M. Andersen, Oliwia Witczak, Trine B. Haugen, Hugo L. Hammer, Pål Halvorsen, Michael A. Riegler

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view.

Stacked dense optical flows and dropout layers to predict sperm motility and morphology

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.

regression

Extracting temporal features into a spatial domain using autoencoders for sperm video analysis

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.

Transfer Learning

Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction

no code implementations • 29 Oct 2019 • Steven A. Hicks, Jorunn M. Andersen, Oliwia Witczak, Vajira Thambawita, Påll Halvorsen, Hugo L. Hammer, Trine B. Haugen, Michael A. Riegler

In the field of male human reproduction, clinical and biological data are not used to its fullest potential.

BIG-bench Machine Learning