Open Collections

Wang, Yuheng

University of British Columbia

Skin disorders are among the most prevalent human diseases, affecting a vast population and posing a significant financial burden on the global healthcare system. Although early treatment may considerably improve the cure rate of skin disorders, limited access to dermatologists and a lack of training among other healthcare workers make early detection difficult. In recent years, deep learning has produced exceptional success in the computer-aided detection of skin disorders. However, some high-performance methods are challenging to practice in complex clinical scenarios because of uneven distribution of data categories, multiple data types with dimensional differences, and a lack of support from clinical experience. Therefore, people have begun to combine efficient algorithms with clinical knowledge to better utilize skin lesion information from multiple modalities in various application scenarios rather than chasing prediction accuracy. Herein, we investigate how to combine clinical expertise with sophisticated algorithms and integrate several common imaging modalities and demographic data to make deep learning more persuasive and acceptable in clinical applications. First, we used deep learning to incorporate clinical domain knowledge. By combining a deep feature extractor with a clinically restricted classifier chain, we offered a unique multi-modal framework for skin cancer diagnosis that mimicked one of the most widely used dermoscopy algorithms, the 7-point checklist. Then, using colour photos, symptom characteristics, and demographic data, we created a multi-modal content-based image retrieval system from an existing skin image database using similarity network fusion and deep community analysis. This research addressed the dimensional discrepancies when several data sources were fused. Then, we investigated the viability of utilizing deep learning in the clinic with limited resources. We proposed a new method for skin disease classification that unified diverse knowledge into a generic knowledge distillation framework. This method significantly improved the performance of lightweight models for portable embedded devices. Finally, considering the small sample size resulting from the high collection cost, we offered a framework based on the patching technique and decision level fusion that completely exploits the features of the polarisation speckle data set with a small number of samples, completing the application of deep learning on small-scale data sets.

Text

2022-09-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82702

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/