  Annotating biomedical images for supervised learning is a complex and
labor-intensive task due to data diversity and its intricate nature. In this
paper, we propose an innovative method, the efficient one-pass selective
annotation (EPOSA), that significantly reduces the annotation burden while
maintaining robust model performance. Our approach employs a variational
autoencoder (VAE) to extract salient features from unannotated images, which
are subsequently clustered using the DBSCAN algorithm. This process groups
similar images together, forming distinct clusters. We then use a two-stage
sample selection algorithm, called representative selection (RepSel), to form a
selected dataset. The first stage is a Markov Chain Monte Carlo (MCMC) sampling
technique to select representative samples from each cluster for annotations.
This selection process is the second stage, which is guided by the principle of
maximizing intra-cluster mutual information and minimizing inter-cluster mutual
information. This ensures a diverse set of features for model training and
minimizes outlier inclusion. The selected samples are used to train a VGG-16
network for image classification. Experimental results on the Med-MNIST dataset
demonstrate that our proposed EPOSA outperforms random selection and other
state-of-the-art methods under the same annotation budget, presenting a
promising direction for efficient and effective annotation in medical image
analysis.
