  Classifying and analyzing human cells is a lengthy procedure, often involving
a trained professional. In an attempt to expedite this process, an active area
of research involves automating cell classification through use of deep
learning-based techniques. In practice, a large amount of data is required to
accurately train these deep learning models. However, due to the sparse human
cell datasets currently available, the performance of these models is typically
low. This study investigates the feasibility of using few-shot learning-based
techniques to mitigate the data requirements for accurate training. The study
is comprised of three parts: First, current state-of-the-art few-shot learning
techniques are evaluated on human cell classification. The selected techniques
are trained on a non-medical dataset and then tested on two out-of-domain,
human cell datasets. The results indicate that, overall, the test accuracy of
state-of-the-art techniques decreased by at least 30% when transitioning from a
non-medical dataset to a medical dataset. Second, this study evaluates the
potential benefits, if any, to varying the backbone architecture and training
schemes in current state-of-the-art few-shot learning techniques when used in
human cell classification. Even with these variations, the overall test
accuracy decreased from 88.66% on non-medical datasets to 44.13% at best on the
medical datasets. Third, this study presents future directions for using
few-shot learning in human cell classification. In general, few-shot learning
in its current state performs poorly on human cell classification. The study
proves that attempts to modify existing network architectures are not effective
and concludes that future research effort should be focused on improving
robustness towards out-of-domain testing using optimization-based or
self-supervised few-shot learning techniques.
