  Developing reliable computational frameworks for early parasite detection,
particularly at the ova (or egg) stage is crucial for advancing healthcare and
effectively managing potential public health crises. While deep learning has
significantly assisted human workers in various tasks, its application and
diagnostics has been constrained by the need for extensive datasets. The
ability to learn from an extremely scarce training dataset, i.e., when fewer
than 5 examples per class are present, is essential for scaling deep learning
models in biomedical applications where large-scale data collection and
annotation can be expensive or not possible (in case of novel or unknown
infectious agents). In this study, we introduce ProtoKD, one of the first
approaches to tackle the problem of multi-class parasitic ova recognition using
extremely scarce data. Combining the principles of prototypical networks and
self-distillation, we can learn robust representations from only one sample per
class. Furthermore, we establish a new benchmark to drive research in this
critical direction and validate that the proposed ProtoKD framework achieves
state-of-the-art performance. Additionally, we evaluate the framework's
generalizability to other downstream tasks by assessing its performance on a
large-scale taxonomic profiling task based on metagenomes sequenced from
real-world clinical data.
