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

Abstract

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.