Classifying images in a histopathological dataset using the cumulative distribution transform on an automata architecture
The Automata Processor is a novel hardware accelerator that enables parallel implementation of classification in large datasets. To date, this pattern matching has been limited to one-dimensional problems that can be implemented as flexible string-matching methods such as those found in genomics. To map our multi-dimensional tissue image classification problem into a string matching processor, we first transform discriminative feature descriptors using a cumulative distribution transform. The transformed features are encoded into regular expressions which can be executed on the automata. The paper highlights a method of evaluating the similarity between images using these regular expressions in the automata processor. Our image retrieval and classification method improves on classification accuracy and achieves a run-time of less than one one-hundredth of a second per image which represents a three-fold improvement over competing architectures.