  Widely used traditional supervised deep learning methods require a large
number of training samples but often fail to generalize on unseen datasets.
Therefore, a more general application of any trained model is quite limited for
medical imaging for clinical practice. Using separately trained models for each
unique lesion category or a unique patient population will require sufficiently
large curated datasets, which is not practical to use in a real-world clinical
set-up. Few-shot learning approaches can not only minimize the need for an
enormous number of reliable ground truth labels that are labour-intensive and
expensive but can also be used to model on a dataset coming from a new
population. To this end, we propose to exploit an optimization-based implicit
model agnostic meta-learning (iMAML) algorithm under few-shot settings for
medical image segmentation. Our approach can leverage the learned weights from
diverse but small training samples to perform analysis on unseen datasets with
high accuracy. We show that, unlike classical few-shot learning approaches, our
method improves generalization capability. To our knowledge, this is the first
work that exploits iMAML for medical image segmentation and explores the
strength of the model on scenarios such as meta-training on unique and mixed
instances of lesion datasets. Our quantitative results on publicly available
skin and polyp datasets show that the proposed method outperforms the naive
supervised baseline model and two recent few-shot segmentation approaches by
large margins. In addition, our iMAML approach shows an improvement of 2%-4% in
dice score compared to its counterpart MAML for most experiments.
