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Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology

  • Mohammad PeikariEmail author
  • Judit Zubovits
  • Gina Clarke
  • Anne L. Martel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

Purpose: Completely labeled datasets of pathology slides are often difficult and time consuming to obtain. Semi-supervised learning methods are able to learn reliable models from small number of labeled instances and large quantities of unlabeled data. In this paper, we explored the potential of clustering analysis for semi-supervised support vector machine (SVM) classifier. Method: A clustering analysis method was proposed to find regions of high density prior to finding the decision boundary using a supervised SVM and was compared with another state-of-the-art semi-supervised technique. Different percentages of labeled instances were used to train supervised and semi-supervised SVM learners from an image dataset generated from 50 whole-mount images (8 patients) of breast specimen. Their cross-validated classification performances were compared with each other using the area under the ROC curve measure. Result: Our proposed clustering analysis for semi-supervised learning was able to produce a reliable classification model from small amounts of labeled data. Comparing the proposed method in this study with a well-known implementation of semi-supervised SVM, our method performed much faster and produced better results.

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Mohammad Peikari
    • 1
    Email author
  • Judit Zubovits
    • 2
  • Gina Clarke
    • 3
  • Anne L. Martel
    • 1
    • 3
  1. 1.Medical BiophysicsUniversity of TorontoTorontoCanada
  2. 2.Faculty of MedicineUniversity of TorontoTorontoCanada
  3. 3.Physical SciencesSunnybrook Research InstituteTorontoCanada

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