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.

在这项研究中，我们提出了一种主动少样本学习框架，名为Myriad Active Learning（MAL），该框架利用对无标签数据的自我监督处理和聚类知识作为激活主动学习循环的基础，并通过优化编码器上的浅层任务网络来改善伪标签的生成和主动学习查询选择过程，进而减少样本的冗余性，并且在两个公共组织病理学数据集上的广泛实验证明，MAL相较于之前的方法具有更高的测试准确性、宏F1分数和标签效率，并且能在只对数据集的5%进行标注的情况下达到与完全监督算法可比的测试准确性。

MyriadAL：组织病理学的主动少样本学习