Tim Lee

The design challenge of improving melanoma care stems from two factors: its incidence which continues to increase, and its lethality if not detected and treated early. In the search for tools that can benefit rather than exacerbate the effort to implement widespread and accessible detection of melanoma, this dissertation embarks on a research program to design a lightweight optical probing technology to quantifiably measure cellular malignancy.Probing skin tissue with a laser generates polarization speckle, a stochastic interference pattern containing both polarization and coherence information, from which tissue morphology metrics such as surface roughness can be extracted. A simple optical probe was developed to investigate polarimetry for rapid melanoma detection, while incorporating considerations for the generation of speckle in its design. This probe was tested in a pilot clinical study on a variety of skin lesions in vivo, where it was found that the mean degree of polarization (DOP±sterr) for melanoma (0.46 ± 0.09) was greater than that of benign nevus (0.31 ± 0.06) and all nonmelanoma lesions (0.28 ± 0.01). This result was expanded upon in an exploration of a new metric called polarization memory rate (PMR). Polarization memory rate is a ratio of circular to linear degrees of polarization, which is based on the scattering and depolarization differences between benign and malignant cells due to their differing optical refractive index. This metric (PMR±sterr) as measured by the probe was able to further separate melanoma (0.97 ± 0.11) from nevus (0.58 ± 0.07) and separate cancerous lesions from benign lesions with p
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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.

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Skin disorders are among the most common healthcare referrals in Canada, affecting a large population and imposing high healthcare costs. Early detection plays an essential role in efficient management and better outcome. However, restricted access to dermatologists and lack of education to other healthcare professionals pose a major challenge for early detection. Computer-aided systems have great potential as viable tools to identify early skin abnormalities. The initial clinical diagnosis phase of most skin disorders involves visual inspection of the lesion for specific features, associated with certain abnormalities. One of the main cutaneous features are vascular structures, which are significantly involved in pathogenesis, diagnosis, and treatment outcome of skin abnormalities. The presence and morphology of cutaneous vessels are suggestive clues for specific abnormalities. However, there has been no systematic approach for comprehensive analysis of skin vasculature. In this thesis, we propose a three-level framework to systematically detect, quantify and analyze the characteristics of superficial cutaneous blood vessels. First, we investigate the vessels at pixel-level. We propose novel techniques for detection (absence/presence) and segmentation of vascular structures in pigmented and non-pigmented lesions and evaluate the performance quantitatively. We develop a fully automatic vessel segmentation framework based on decomposing the skin into its component chromophores and accounting for shape. Furthermore, we design a deep learning framework based on stacked sparse auto-encoders for detection and localization of skin vasculature. Compared to previous studies, we achieve higher detection performance, while preserving clinical feature interpretability.Next, we analyze the vessels at lesion-level. We propose a novel set of architectural, geometrical and topological features to differentiate vascular morphologies. The defined feature set can effectively differentiate four major classes of vascular patterns.Finally, we investigate the vessels at disease-level. We analyze the relationship between vascular characteristics and disease diagnosis. We design and deploy novel features to evaluate total blood content and vascular characteristics of the lesion to differentiate cancerous lesions from benign ones. We also build a system upon integrating patient’s clinical information and lesion’s visual characteristics using deep feature learning, which achieves superior cancer classification performance compared to current techniques without the need for handcrafted high-level features.

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