  Time-lapse is a technology used to record the development of embryos during
in-vitro fertilization (IVF). Accurate classification of embryo early
development stages can provide embryologists valuable information for assessing
the embryo quality, and hence is critical to the success of IVF. This paper
proposes a multi-task deep learning with dynamic programming (MTDL-DP) approach
for this purpose. It first uses MTDL to pre-classify each frame in the
time-lapse video to an embryo development stage, and then DP to optimize the
stage sequence so that the stage number is monotonically non-decreasing, which
usually holds in practice. Different MTDL frameworks, e.g., one-to-many,
many-to-one, and many-to-many, are investigated. It is shown that the
one-to-many MTDL framework achieved the best compromise between performance and
computational cost. To our knowledge, this is the first study that applies MTDL
to embryo early development stage classification from time-lapse videos.
