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.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Salem, Milad, "Practical Deep Learning: Utilization of Selective Transfer Learning for Biomedical Applications" (2022). Electronic Theses and Dissertations, 2020-. 1487.
Restricted to the UCF community until November 2027; it will then be open access.