Selecting Euploid Embryos for Transfer by Preimplantation Genetic Testing with the Help of Next-Generation Sequencing in Poor Prognosis Patients: A Retrospective Cohort Analysis.

Background: The current embryo selection methods rely on subjective grading of embryo morphology or a real-time monitoring of the embryonic development and assessment of multiple quantitative endpoints. Even up to 40% of morphologically normal embryos harbour aneuploidies. Preimplantation genetic testing (PGT) is a technology, which gives opportunity to identify euploid embryos before implantation.

Ensembled Transfer Learning and Multiple Kernel Learning for Phonocardiogram Based Atherosclerotic Coronary Artery Disease Detection.

Conventional machine learning has paved the way for a simple, affordable, non-invasive approach for Coronary artery disease (CAD) detection using phonocardiogram (PCG). It leaves a scope to explore improvement of performance metrics by fusion of learned representations from deep learning. In this study, we propose a novel, multiple kernel learning (MKL) for their fusion using deep embeddings transferred from pre-trained convolutional neural network (CNN).