1. A method for automated cell classification, comprising: acquiring a series of time-sequential images of at least one human embryo comprising one or more cells contained in a multi-well culture dish comprising a plurality of microwells with a camera of at least one time-lapse microscope; extracting image-based features from the series of time-sequential images; applying a model to the image-based features; applying a plurality of first classifiers to each of the series of time-sequential images of at least one human embryo to determine, for each image, a first classification probability associated with each first classifier, wherein: each first classifier is associated with a distinct first number of cells, and determines the first classification probability for the each image based on a plurality of cell features including one or more machine learned cell features; and the first classification probability indicates a first estimated likelihood that the distinct first number of cells associated with the each first classifier is shown in the each image, the each of the series of time-sequential images thereby having a plurality of the first classification probabilities associated therewith; and classifying each image as showing a second number of cells based on the distinct first number of cells associated with the each first classifier and the plurality of first classification probabilities associated therewith, wherein the classification of each image as showing a second number of cells comprises one of the extracted image-based features; generating an aneuploidy prediction for the at least one human embryo with the model and the image-based features.