Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation
This work addresses active learning for medical segmentation, offering incremental improvements in efficiency for biomedical applications.
The paper tackled the problem of active learning in biomedical segmentation by exploring hybrid models combining entropy-based and representativeness sampling, specifically using UMAP for representativeness. It found that a novel Entropy-UMAP hybrid achieved statistically significant Dice score improvements over a random baseline (3.2% for cardiac, 4.5% for prostate) and outperformed 10 other active learning methods.
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.