Abstract
In this post-genomic era, microscopic imaging is playing a crucial role in biomedical research and important information is to be discovered by quantitatively mining the resulting massive imagery databases. To this end, an important prerequisite is robust, high quality imagery databases. This is because defect images will jeopardize downstream tasks such as feature extraction and statistical analysis, yielding misleading results or even false conclusions. This paper presents a weakly supervised learning framework to tackle this problem. Our framework resembles a cascade of classifiers with feature and similarity measure designed for both global and local defects. We evaluated the framework on a database of images and obtained a 96.9% F-score for the important normal class. Click-and-play open source software is provided.
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Lou, X., Fiaschi, L., Koethe, U., Hamprecht, F.A. (2012). Quality Classification of Microscopic Imagery with Weakly Supervised Learning. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_22
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DOI: https://doi.org/10.1007/978-3-642-35428-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35427-4
Online ISBN: 978-3-642-35428-1
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