Assessing Representation Learning and Clustering Algorithms for Computer-Assisted Image Annotation-Simulating and Benchmarking MorphoCluster.

ABSTRACT: Image annotation is a time-consuming and costly task. Previously, we published MorphoCluster as a novel image annotation tool to address problems of conventional, classifier-based image annotation approaches: their limited efficiency, training set bias and lack of novelty detection. MorphoCluster uses clustering and similarity search to enable efficient, computer-assisted image annotation. In this work, we provide a deeper analysis of this approach. We simulate the actions of a MorphoCluster user to avoid extensive manual annotation runs. This simulation is used to test supervised, unsupervised and transfer representation learning approaches. Furthermore, shrunken k-means and partially labeled k-means, two new clustering algorithms that are tailored specifically for the MorphoCluster appro

SUBMITTER: Schroder SM 

PROVIDER: S-EPMC9003521 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature