title: A resampling-based Markovian model for automated colon cancer diagnosis.
abstract: In recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image variations. In order to meet this challenge, it is important to learn the systems on a large number of labeled images from a different range of variation. However, acquiring labeled images is quite difficult in this domain, and hence, the labeled training data are typically very limited. Although the issue of having limited labeled data is acknowledged by many researchers, it has rarely been considered in the system design. This paper successfully addresses this issue, introducing a new resampling framework to simulate variations in tissue images. This framework generates multiple sequences from an image for its representation and models them using a Markov process. Working with colon tissue images, our experiments show that this framework increases the generalization capacity of a learner by increasing the size and variation of the training data and improves the classification performance of a given image by combining the decisions obtained on its sequences.
authors: Department of Computer Engineering, Bilkent University, Ankara TR-06800, Turkey. erdemo@cs.bilkent.edu.tr
authors: Erdem
authors: E
authors: Ozdemir
authors: Cenk
authors: C
authors: Sokmensuer
authors: Cigdem
authors: C
authors: Gunduz-Demir
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003110:Colonic Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D008853:Microscopy / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity