In this work, we study various hybrid models of entropy-based and
representativeness sampling techniques in the context of active learning in
medical segmentation, in particular examining the role of UMAP (Uniform
Manifold Approximation and Projection) as a technique for capturing
representativeness. Although UMAP has been shown viable as a general purpose
dimension reduction method in diverse areas, its role in deep learning-based
medical segmentation has yet been extensively explored. Using the cardiac and
prostate datasets in the Medical Segmentation Decathlon for validation, we
found that a novel hybrid combination of Entropy-UMAP sampling technique
achieved a statistically significant Dice score advantage over the random
baseline ($3.2 \%$ for cardiac, $4.5 \%$ for prostate), and attained the
highest Dice coefficient among the spectrum of 10 distinct active learning
methodologies we examined. This provides preliminary evidence that there is an
interesting synergy between entropy-based and UMAP methods when the former
precedes the latter in a hybrid model of active learning.

本研究探讨了在医学分割的主动学习领域中，熵和代表性采样技术的混合模型，特别是研究了 UMAP（均匀流形逼近和投影）作为捕捉代表性的技术的作用。通过在医学分割十项挑战中使用心脏和前列腺数据集进行验证，我们发现新颖的熵 - UMAP 采样技术的混合组合在 Dice 分数上显著优于随机基线（心脏为 3.2％，前列腺为 4.5％），并在我们研究的 10 种不同主动学习方法中获得了最高的 Dice 系数。这提供了初步证据表明，在混合主动学习模型中，熵和 UMAP 方法之间存在着有趣的协同作用，其中前者在后者之前起到了积极的作用。