Active learning framework with iterative clustering for bioimage classification

Natsumaro Kutsuna, Takumi Higaki, Sachihiro Matsunaga (), Tomoshi Otsuki, Masayuki Yamaguchi, Hirofumi Fujii and Seiichiro Hasezawa
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Natsumaro Kutsuna: Graduate School of Frontier Sciences, University of Tokyo
Takumi Higaki: Graduate School of Frontier Sciences, University of Tokyo
Sachihiro Matsunaga: Faculty of Science and Technology, Tokyo University of Science
Tomoshi Otsuki: Graduate School of Information Science and Technology, University of Tokyo
Masayuki Yamaguchi: Research Center for Innovative Oncology, National Cancer Center Hospital East
Hirofumi Fujii: Research Center for Innovative Oncology, National Cancer Center Hospital East
Seiichiro Hasezawa: Graduate School of Frontier Sciences, University of Tokyo

Nature Communications, 2012, vol. 3, issue 1, 1-10

Abstract: Abstract Advances in imaging systems have yielded a flood of images into the research field. A semi-automated facility can reduce the laborious task of classifying this large number of images. Here we report the development of a novel framework, CARTA (Clustering-Aided Rapid Training Agent), applicable to bioimage classification that facilitates annotation and selection of features. CARTA comprises an active learning algorithm combined with a genetic algorithm and self-organizing map. The framework provides an easy and interactive annotation method and accurate classification. The CARTA framework enables classification of subcellular localization, mitotic phases and discrimination of apoptosis in images of plant and human cells with an accuracy level greater than or equal to annotators. CARTA can be applied to classification of magnetic resonance imaging of cancer cells or multicolour time-course images after surgery. Furthermore, CARTA can support development of customized features for classification, high-throughput phenotyping and application of various classification schemes dependent on the user's purpose.

Date: 2012
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DOI: 10.1038/ncomms2030

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