Dissertation/Thesis Abstract

Feature Learning as a Tool to Identify Existence of Multiple Biological Patterns
by Patsekin, Aleksandr, M.S., Purdue University, 2018, 69; 10807747
Abstract (Summary)

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.

Indexing (document details)
Advisor: McCartney, William G., Robinson, Joseph P.
Commitee: Rajwa, Bartek, Springer, John
School: Purdue University
Department: Computer and Information Technology
School Location: United States -- Indiana
Source: MAI 57/06M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Bioinformatics, Artificial intelligence, Computer science
Keywords: Autoencoder, Deep learning, Feature learning, Pattern recognition, Unsupervised learning
Publication Number: 10807747
ISBN: 9780438012509
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