标题： 牛胚胎从视频显微镜中转移性的早期预测 显示英文标题

标题： Early prediction of the transferability of bovine embryos from videomicroscopy

摘要： 视频显微技术是一种有前途的工具，结合机器学习，用于研究体外受精牛胚胎的早期发育并尽快评估其可转移性。 我们旨在最多四天内预测胚胎的可转移性，以2D时间推移显微视频作为输入。 我们将这个问题形式化为一个监督二分类问题，分为可转移和不可转移两类。 挑战有三个方面：1）外观和运动区分不明显，2）类别模糊，3）标注数据量小。 我们提出了一种涉及三个路径的3D卷积神经网络，使其在时间上具有多尺度，并能够以不同方式处理外观和运动。 在训练中，我们保留了焦点损失。 我们的模型名为SFR，与其他方法相比表现更优。 实验证明了它在我们具有挑战性的生物任务中的有效性和准确性。 显示英文摘要

摘要： 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.