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Image classification of vascular smooth muscle cells

Published: 11 November 2010 Publication History

Abstract

The traditional method of cell microscopy can be subjective, due to observer variability, a lack of standardization, and a limited feature set. To address this challenge, we developed an image classifier using a machine learning approach. Our system was able to classify cytoskeletal changes in A10 rat smooth muscle cells with an accuracy of 85% to 99%. These cytoskeletal changes correspond to cell-to-matrix interactions. Analysis of these changes may be used to better understand how these interactions correspond to certain physiologic processes.

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cover image ACM Other conferences
IHI '10: Proceedings of the 1st ACM International Health Informatics Symposium
November 2010
886 pages
ISBN:9781450300308
DOI:10.1145/1882992
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 November 2010

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  1. cell biology
  2. digital image processing
  3. machine learning

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IHI '10
IHI '10: ACM International Health Informatics Symposium
November 11 - 12, 2010
Virginia, Arlington, USA

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