Abstract

Over the recent years, deep learning has risen in popularity due to its capabilities in learning from data and extracting features from it in an automatic manner during training. This automatic feature extraction can be a useful tool in domains which require subject-matter-experts to manually or algorithmically extract features from the data, such as in the biomedical domain. However, automatic feature extraction requires a large amount of data, which in turn makes deep learning models data-hungry. This is a challenge for adoption of deep learning to these domains which often have small amounts of training data. In this work, deep learning is implemented in the biomedical and expert-based domains in a practical manner. Through selective transfer learning, learned knowledge from other related or unrelated datasets and tasks are transferred to the target domain, alleviating the problem of low training data. Transfer learning is studied as pre-trained model transfer or off-the-shelf feature extractor transfer in expert-based domains such as drug discovery, electrocardiogram signal arrhythmia detection, and biometric recognition. The results demonstrate that deep learning's automatic feature extraction out-performs traditional expert-made features. Moreover, transfer learning stabilizes the training when low amount of data is present and enables transfer of useful knowledge and patterns to the target domain which results in better feature extraction. Having better features or higher performance in these domains can translate to real-world changes, ranging from finding a suitable drug candidate in a timely manner, to not miss-diagnosing an Electrocardiogram arrhythmia.

Notes

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Graduation Date

2022

Semester

Spring

Advisor

Yuan, Jiann-Shiun

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0009458; DP0027181

URL

https://purls.library.ucf.edu/go/DP0027181

Language

English

Release Date

November 2027

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

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