  With rising male infertility, sperm head morphology classification becomes
critical for accurate and timely clinical diagnosis. Recent deep learning (DL)
morphology analysis methods achieve promising benchmark results, but leave
performance and robustness on the table by relying on limited and possibly
noisy class labels. To address this, we introduce a new DL training framework
that leverages anatomical and image priors from human sperm microscopy crops to
extract useful features without additional labeling cost. Our core idea is to
distill sperm head information with reliably-generated pseudo-masks and
unsupervised spatial prediction tasks. The predicted foreground masks from this
distillation step are then leveraged to regularize and reduce image and label
noise in the tuning stage. We evaluate our new approach on two public sperm
datasets and achieve state-of-the-art performances (e.g. 65.9% SCIAN accuracy
and 96.5% HuSHeM accuracy).
