This paper introduces a novel approach for assessing multiple patterns in biological imaging datasets. The developed tool should be able to provide most probable structure of a dataset of images that consists of biological patterns not encountered during the model training process. The tool includes two major parts: (1) feature learning and extraction pipeline and (2) subsequent clustering with estimation of number of classes. The feature-learning part includes two deep-learning techniques and a feature quantitation pipeline as a benchmark method. Clustering includes three non-parametric methods. K-means clustering is employed for validation and hypothesis testing by comparing results with provided ground truth. The most appropriate methods and hyper-parameters were suggested to achieve maximum clustering quality. A convolutional autoencoder demonstrated the most stable and robust results: entropy-based V-measure metric 0.9759 on a dataset of classes employed for training and 0.9553 on a dataset of completely novel classes.
|Advisor:||McCartney, William G., Robinson, Joseph P.|
|Commitee:||Rajwa, Bartek, Springer, John|
|Department:||Computer and Information Technology|
|School Location:||United States -- Indiana|
|Source:||MAI 57/06M(E), Masters Abstracts International|
|Subjects:||Bioinformatics, Artificial intelligence, Computer science|
|Keywords:||Autoencoder, Deep learning, Feature learning, Pattern recognition, Unsupervised learning|
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