Cancer cell detection and classification using transformation invariant template learning methods

In traditional cancer cell detection, pathologists examine biopsies to make diagnostic assessments, largely based on cell morphology and tissue distribution. The process of image acquisition is very much subjective and the pattern undergoes unknown or random transformations during data acquisition (e.g. variation in illumination, orientation, translation and perspective) results in high degree of variability. Transformed Component Analysis (TCA) incorporates a discrete, hidden variable that accounts for transformations and uses the Expectation Maximization (EM) algorithm to jointly extract components and normalize for transformations. Further the TEMPLAR framework developed takes advantage of hierarchical pattern models and adds probabilistic modeling for local transformations. Pattern classification is based on Expectation Maximization algorithm and General Likelihood Ratio Tests (GLRT). Performance of TEMPLAR is certainly improved by defining area of interest on slide a priori. Performance can be further enhanced by making the kernel function adaptive during learning. (author)