Optics And Computer Vision For Biomedical Applications

Bioengineering is at the cross sections of biology, clinical technology, electrical engineering, computer science and many other domains. The smooth translation of domain technologies to clinics is not just about accuracy and practicality of the technology. It also has to take into account the accessibility (cost and portability), the patients’ comfort and the ease to adapt into the workflow of medical professionals. The dissertation will explore three projects, (1) portable and low-cost near infrared florescence imaging system on mobile phone platform, (2) computer aided diagnosis software for diagnosing chronical kidney disease based on optical coherence tomography (OCT) images and (3) the tracking and localization of hand-held medical imaging probe. These projects aim to translate and adapt modern computation hardware, data analysis models and computer vision technologies to solve and refine clinical diagnosis applications. The dissertation will discuss how the translation, tradeoffs and refinement of those technologies can bring a positive impact on the accuracy, ease of conduct, accessibility and patients’ comfort to the clinical applications

Deep Learning in Visual Computing and Signal Processing

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

High Resolution Maps of the Vasculature of An Entire Organ

The structure of vascular networks represents a great, unsolved problem in anatomy. Network geometry and topology differ dramatically from left to right and person to person as evidenced by the superficial venation of the hands and the vasculature of the retinae. Mathematically, we may state that there is no conserved topology in vascular networks. Efficiency demands that these networks be regular on a statistical level and perhaps optimal. We have taken the first steps towards elucidating the principles underlying vascular organization, creating the rst map of the hierarchical vasculature (above the capillaries) of an entire organ. Using serial blockface microscopy and fluorescence imaging, we are able to identify vasculature at 5 μm resolution. We have designed image analysis software to segment, align, and skeletonize the resulting data, yielding a map of the individual vessels. We transformed these data into a mathematical graph, allowing computationally efficient storage and the calculation of geometric and topological statistics for the network. Our data revealed a complexity of structure unexpected by theory. We observe loops at all scales that complicate the assignment of hierarchy within the network and the existence of set length scales, implying a distinctly non-fractal structure of components within

CREATE: Concept Representation and Extraction from Heterogeneous Evidence

Traditional information retrieval methodology is guided by document retrieval paradigm, where relevant documents are returned in response to user queries. This paradigm faces serious drawback if the desired result is not explicitly present in a single document. The problem becomes more obvious when a user tries to obtain complete information about a real world entity, such as person, company, location etc. In such cases, various facts about the target entity or concept need to be gathered from multiple document sources. In this work, we present a method to extract information about a target entity based on the concept retrieval paradigm that focuses on extracting and blending information related to a concept from multiple sources if necessary. The paradigm is built around a generic notion of concept which is defined as any item that can be thought of as a topic of interest. Concepts may correspond to any real world entity such as restaurant, person, city, organization, etc, or any abstract item such as news topic, event, theory, etc. Web is a heterogeneous collection of data in different forms such as facts, news, opinions etc. We propose different models for different forms of data, all of which work towards the same goal of concept centric retrieval. We motivate our work based on studies about current trends and demands for information seeking. The framework helps in understanding the intent of content, i.e. opinion versus fact. Our work has been conducted on free text data in English. Nevertheless, our framework can be easily transferred to other languages