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Feature Learning as a Tool to Identify Existence of Multiple Biological Patterns
Başlık:
Feature Learning as a Tool to Identify Existence of Multiple Biological Patterns
Yazar:
Patsekin, Aleksandr, author.
ISBN:
9780438012509
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (69 pages)
Genel Not:
Source: Masters Abstracts International, Volume: 57-06M(E).
Advisors: William G. McCartney; Joseph P. Robinson Committee members: Bartek Rajwa; John Springer.
Özet:
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.
Notlar:
School code: 0183
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(691235.1) | 691235-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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