MyriadAL: Active Few Shot Learning for Histopathology
Nico Schiavone Jingyi Wang Shuangzhi Li Roger Zemp Xingyu Li
Active Learning (AL) and Few Shot Learning (FSL) are two label-efficient
methods which have achieved excellent results recently. However, most prior
arts in both learning paradigms fail to explore the wealth of the vast
unlabelled data. In this study, we address this issue in the scenario where the
annotation budget is very limited, yet a large amount of unlabelled data for
the target task is available. We frame this work in the context of
histopathology where labelling is prohibitively expensive. To this end, we
introduce an active few shot learning framework, Myriad Active Learning (MAL),
including a contrastive-learning encoder, pseudo-label generation, and novel
query sample selection in the loop. Specifically, we propose to massage
unlabelled data in a self-supervised manner, where the obtained data
representations and clustering knowledge form the basis to activate the AL
loop. With feedback from the oracle in each AL cycle, the pseudo-labels of the
unlabelled data are refined by optimizing a shallow task-specific net on top of
the encoder. These updated pseudo-labels serve to inform and improve the active
learning query selection process. Furthermore, we introduce a novel recipe to
combine existing uncertainty measures and utilize the entire uncertainty list
to reduce sample redundancy in AL. Extensive experiments on two public
histopathology datasets show that MAL has superior test accuracy, macro
F1-score, and label efficiency compared to prior works, and can achieve a
comparable test accuracy to a fully supervised algorithm while labelling only
5% of the dataset.