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Automated data augmentation, which aims at engineering augmentation policyautomatically, recently draw a growing research interest. Many previousauto-augmentation methods utilized a Density Matching strategy by evaluatingpolicies in terms of the test-time augmentation performance. In this paper, wetheoretically and empirically demonstrated the inconsistency between the trainand validation set of small-scale medical image datasets, referred to asin-domain sampling bias. Next, we demonstrated that the in-domain sampling biasmight cause the inefficiency of Density Matching. To address the problem, animproved augmentation search strategy, named Augmented Density Matching, wasproposed by randomly sampling policies from a prior distribution for training.Moreover, an efficient automatical machine learning(AutoML) algorithm wasproposed by unifying the search on data augmentation and neural architecture.Experimental results indicated that the proposed methods outperformedstate-of-the-art approaches on MedMNIST, a pioneering benchmark designed forAutoML in medical image analysis.

Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture