The present invention discloses an IVF-ET
embryo selection method based on time-difference imaging multi-
task learning, belonging to the field of image
data processing and assisted
reproduction. The method comprises the following steps: first, using a coding module, effectively extracting spatiotemporal features in
embryo time-difference imaging sequences through an expanded three-dimensional convolutional network and a bidirectional long short-
term memory network; then, within a multi-
task learning framework with shared hard parameters, utilizing shared feature representations, and simultaneously predicting multiple key indicators such as a quality grade based on the Istanbul
consensus and a developmental grade based on the Gardner grade through multiple task-specific predictors; during the training process, the AdaTask optimization
algorithm and the Frank-Wolfe method are used to optimize
model parameters, and a virtual predictor mechanism is introduced to enhance the model's versatility; the present invention can more accurately, objectively, and efficiently assess
embryo quality and developmental potential, reduce the burden on embryologists, and improve the success rate of IVF and ET, and can be applied to fields such as
animal husbandry and
endangered species protection.