Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches

The remarkable advancements in deep learning methodologies over recent years can be attributed to the availability of large, high-quality labeled datasets, intricate network structures, and the swift progression of hardware technologies. However, in the biomedical engineering, the scarcity of data o...

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Main Author: Yuan, Liqiang
Other Authors: Mohammed Yakoob Siyal
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/177418
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1774182024-06-03T06:51:19Z Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches Yuan, Liqiang Mohammed Yakoob Siyal School of Electrical and Electronic Engineering EYAKOOB@ntu.edu.sg Computer and Information Science domain adaptation EEG classification medical image segmentation The remarkable advancements in deep learning methodologies over recent years can be attributed to the availability of large, high-quality labeled datasets, intricate network structures, and the swift progression of hardware technologies. However, in the biomedical engineering, the scarcity of data often poses a significant challenge in training a well-generalized model. This thesis delves into the potential of transfer learning methods as a viable solution to this data scarcity issue. The research provides a comprehensive exploration of Transfer Learning (TL), Unsupervised Domain Adaptation (UDA), Source-Free Domain Adaptation (SFDA), and Black-Box Domain Adaptation (BBDA) within the realm of biomedical engineering. It tackles the issue of data dependency in deep learning, with a particular emphasis on the application of TL in fine-tuning neural networks for specialized tasks. The study further investigates UDA techniques, specifically in the context of cross-dataset EEG classification tasks, and proposes entropy minimization-based methodologies to alleviate domain shifts. The thesis also scrutinizes SFDA in the context of medical imaging segmentation and abnormal ECG classification, introducing three innovative methodologies to overcome inherent challenges. Expanding on SFDA, the thesis evaluates the efficiency and accuracy of BBDA in real-time applications for cross-subject EEG driver drowsiness detection. This research contributes novel insights into handling data dependency, domain shifts, and privacy concerns in biomedical engineering, advocating for a nuanced approach where the scope of achievements is judiciously balanced against the challenges posed by scarce data. Doctor of Philosophy 2024-05-24T13:07:54Z 2024-05-24T13:07:54Z 2024 Thesis-Doctor of Philosophy Yuan, L. (2024). Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177418 https://hdl.handle.net/10356/177418 10.32657/10356/177418 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
domain adaptation
EEG classification
medical image segmentation
spellingShingle Computer and Information Science
domain adaptation
EEG classification
medical image segmentation
Yuan, Liqiang
Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches
description The remarkable advancements in deep learning methodologies over recent years can be attributed to the availability of large, high-quality labeled datasets, intricate network structures, and the swift progression of hardware technologies. However, in the biomedical engineering, the scarcity of data often poses a significant challenge in training a well-generalized model. This thesis delves into the potential of transfer learning methods as a viable solution to this data scarcity issue. The research provides a comprehensive exploration of Transfer Learning (TL), Unsupervised Domain Adaptation (UDA), Source-Free Domain Adaptation (SFDA), and Black-Box Domain Adaptation (BBDA) within the realm of biomedical engineering. It tackles the issue of data dependency in deep learning, with a particular emphasis on the application of TL in fine-tuning neural networks for specialized tasks. The study further investigates UDA techniques, specifically in the context of cross-dataset EEG classification tasks, and proposes entropy minimization-based methodologies to alleviate domain shifts. The thesis also scrutinizes SFDA in the context of medical imaging segmentation and abnormal ECG classification, introducing three innovative methodologies to overcome inherent challenges. Expanding on SFDA, the thesis evaluates the efficiency and accuracy of BBDA in real-time applications for cross-subject EEG driver drowsiness detection. This research contributes novel insights into handling data dependency, domain shifts, and privacy concerns in biomedical engineering, advocating for a nuanced approach where the scope of achievements is judiciously balanced against the challenges posed by scarce data.
author2 Mohammed Yakoob Siyal
author_facet Mohammed Yakoob Siyal
Yuan, Liqiang
format Thesis-Doctor of Philosophy
author Yuan, Liqiang
author_sort Yuan, Liqiang
title Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches
title_short Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches
title_full Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches
title_fullStr Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches
title_full_unstemmed Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches
title_sort domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177418
_version_ 1800916108551651328