A Systematic Review of Few-Shot Learning in Medical Imaging
Eva Pachetti Sara Colantonio
The lack of annotated medical images limits the performance of deep learning
models, which usually need large-scale labelled datasets. Few-shot learning
techniques can reduce data scarcity issues and enhance medical image analysis,
especially with meta-learning. This systematic review gives a comprehensive
overview of few-shot learning in medical imaging. We searched the literature
systematically and selected 80 relevant articles published from 2018 to 2023.
We clustered the articles based on medical outcomes, such as tumour
segmentation, disease classification, and image registration; anatomical
structure investigated (i.e. heart, lung, etc.); and the meta-learning method
used. For each cluster, we examined the papers' distributions and the results
provided by the state-of-the-art. In addition, we identified a generic pipeline
shared among all the studies. The review shows that few-shot learning can
overcome data scarcity in most outcomes and that meta-learning is a popular
choice to perform few-shot learning because it can adapt to new tasks with few
labelled samples. In addition, following meta-learning, supervised learning and
semi-supervised learning stand out as the predominant techniques employed to
tackle few-shot learning challenges in medical imaging and also best
performing. Lastly, we observed that the primary application areas
predominantly encompass cardiac, pulmonary, and abdominal domains. This
systematic review aims to inspire further research to improve medical image
analysis and patient care.