Microscopic images with increased scale and content call for high performance computing when applying automatic tools for biological image analysis. Speed of analysis can be improved at various stages. In learning-based models, selecting suitable algorithms for a given problem can be a lengthy process given the large pool of algorithms and the variety of biological problems. In this paper, we describe a portable method for efficiently and adaptively selecting an effective model for biological image classification as a step toward the goal of achieving high throughput biological image analysis. We implemented a high performance tool which extends the bioimage classification and annotation platform BIOCAT by deploying the model selection process on a cluster using a distributed design based on remote method invocation. The high performance model selection, when tested and compared using ten benchmarking data sets, is shown to not only dramatically increase the speed of the learning process, but also bring improved accuracy to several state-of-the-art data sets for bioimage classification. These achievements are attributed to the combined power of BIOCAT's adaptive model selection as well as the capability of distributed model evaluation. The tool is deployable to various types of distributed environments.
['Jie Zhou', 'Anthony Brunson', 'John Winans', 'Kirk L. Duffin', 'Nicholas T. Karonis']
Performance Model Selection for Learning-based Biological Image Analysis on a Cluster