Year: 2024
Type: conference-paper
Abstract: Videomicroscopy is a promising tool combined with machine learning for studying the early development of in vitro fertilized bovine embryos and assessing its transferability as soon as possible. We aim to predict the embryo transferability within four days at most, taking 2D time-lapse microscopy videos as input. We formulate this problem as a supervised binary classification problem for the classes transferable and not transferable. The challenges are three-fold: 1) poorly discriminating appearance and motion, 2) class ambiguity, 3) small amount of annotated data. We propose a 3D convolutional neural network involving three pathways, which makes it multi-scale in time and able to handle appearance and motion in different ways. For training, we retain the focal loss. Our model, named SFR, compares favorably to other methods. Experiments demonstrate its effectiveness and accuracy for our challenging biological task.
Institutions: Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, Université de Rennes, Université de Versailles Saint-Quentin-en-Yvelines, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Biologie de la Reproduction, Environnement, Epigénétique et Développement, École Nationale Vétérinaire d'Alfort
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4402916507