3 June 2008
How can a computer learn to see? Machine learning for image categorization and computational pathology

Joachim M Buhmann
Institute for Computational Science Department of Computer Science Swiss Federal Institute of Technology (ETH) Zurich

Vision with its grand challenge of general scene understanding requires hierachically structured, modular representations of image content. Models for scene understanding also have to capture the statistical nature of images with their enormeous variability and semantic richness. Compositionality as a design principle advocates a representation scheme of image content which detects local parts like wheels for cars or eyes for faces and composes these information pieces to combinations of parts in a recursive manner. Graphical models can express both the probabilistic nature of features as well as their spatial relations. State-of-the-art categorization results both for still images and for video are achieved with a significantly more succinct representation than employed by alternative approaches. Equally complicated detection problems arise in medical imaging where e.g. Renal cell carcinoma (RCC) tissue has to be graded on the basis of immunohistochemical staining to estimate the progression of cancer. We propose a completely automated image analysis pipeline to predict the survival of RCC patients based on the analysis of immunohistochemical staining of MIB-1 on tissue microarrays. A random forest classifier detects cell nuclei of cancerous cells and predicts their staining. The application to a test set of 133 patients clearly demonstrate that our computational pathology analysis matches the prognostic performance of expert pathologists

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