Embryo quality assessment is vital in assisted reproductive technology, aiding specialists in quickly selecting the most viable embryos for implantation. Leveraging the power of Deep Learning methods enables quick and accurate prediction, reducing the workload of medical specialists and increasing their productivity. Deep Learning algorithms learn recurring patterns and feature representations for a given training dataset, being able to generalize and perform predictions on previously unseen testing data. However, Deep Learning models are prone to be biased in the event of imbalanced training data, which is a common problem for many datasets. We have used batch-level data balancing to ameliorate the effect of data imbalance and employed data augmentation techniques, transfer learning, test time augmentation and model ensembles in order to compare the performance of multiple Deep Learning training configurations on the Embryo Fertility Classification task.
This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE