Resampling-based Markovian modeling for automated cancer diagnosis
Author(s)
Advisor
Demir, Çiğdem GündüzDate
2011Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Correct diagnosis and grading of cancer is very crucial for planning an effective
treatment. However, cancer diagnosis on biopsy images involves visual interpretation
of a pathologist, which is highly subjective. This subjectivity may, however,
lead to selecting suboptimal treatment plans. In order to circumvent this problem,
it has been proposed to use automatic diagnosis and grading systems that
help decrease the subjectivity levels by providing quantitative measures. However,
one major challenge for designing these systems is the existence of high
variance observed in the biopsy images due to the nature of biopsies. Thus, for
successful classifications of unseen images, these systems should be trained with
a large number of labeled images. However, most of the training sets in this
domain have limited size of labeled data since it is quite difficult to collect and
label histopathological images. In this thesis, we successfully address this issue
by presenting a new resampling framework. This framework relies on increasing
the generalization capacity of a classifier by augmenting the size and variation
in the training set. To this end, we generate multiple sequences from an image,
each of which corresponds to a perturbed sample of the image. Each perturbed
sample characterizes different parts of the image, and hence, they are slightly
different from each other. The use of these perturbed samples for representing
the image increases the size and variability of the training set. These samples are
modeled with Markov processes which are used to classify unseen image. Working
with histopathological tissue images, our experiments demonstrate that the
proposed framework is more effective for both larger and smaller training sets
compared against other approaches. Additionally, they show that the use of perturbed
samples is effective in a voting scheme which boosts the performance of
the classifier.
Keywords
Histopathological image analysisAutomated cancer diagnosis
Resampling
Markov models
Cancer