Image Classification with Rejection using Contextual Information
This addresses the need for robust classifiers in medical imaging applications where not all parts of an image require classification, though it appears incremental.
The paper tackles image classification with rejection by incorporating multiscale contextual information, resulting in improved classification performance for H&E-stained teratoma tissue images.
We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information. Rejection is desired in image-classification applications that require a robust classifier but not the classification of the entire image. The proposed algorithm combines local and multiscale contextual information with rejection, improving the classification performance. As a probabilistic model for classification, we adopt a multinomial logistic regression. The concept of rejection with contextual information is implemented by modeling the classification problem as an energy minimization problem over a graph representing local and multiscale similarities of the image. The rejection is introduced through an energy data term associated with the classification risk and the contextual information through an energy smoothness term associated with the local and multiscale similarities within the image. We illustrate the proposed method on the classification of images of H&E-stained teratoma tissues.