COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Active learning for Hidden Markov Models: objective functions and algorithms
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets
IEEE Transactions on Image Processing
Compressed sensing for digital holographic microscopy
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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We propose a mathematical framework and algorithms both to build accurate models of fluorescence microscope time series, as well as to design intelligent acquisition systems based on these models. Model building allows the information contained in the 2-D and 3-D time series to be presented in a more useful and concise form than the raw image data. This is particularly relevant as the trend in biology tends more and more towards high-throughput applications, and the resulting increase in the amount of acquired image data makes visual inspection impractical. The intelligent acquisition system uses an active learning approach to choose the acquisition regions that let us build our model most efficiently, resulting in a shorter acquisition time, as well as a reduction of the amount of photobleaching and phototoxicity incurred during acquisition. We validate our methodology by modeling object motion within a cell. For intelligent acquisition, we propose a set of algorithms to evaluate the information contained in a given acquisition region, as well as the costs associated with acquiring this region in terms of the resulting photobleaching and phototoxicity and the amount of time taken for acquisition. We use these algorithms to determine an acquisition strategy: where and when to acquire, as well as when to stop acquiring. Results, both on synthetic as well as real data, demonstrate accurate model building and large efficiency gains during acquisition.