Analyzing time series from eye tracking using Symbolic Aggregate Approximation

This thesis explores the viability of transforming the data produced when tracking the eyes into a discrete symbolic representation. For this transformation, we utilize Symbolic Aggregate Approximation to investigate a new possibility for effectively categorizing data collected via eye tracking technologies. This categorization illustrates tendencies for, e.g., tracking problems, problems with the set-up, normal vision, or vision disturbances. Accordingly, this will contribute to evaluating the eyes' performance and allow professionals to develop a diagnosis based on evidence from objective measurements. The results are based on implementing a symbolic discretization method applied to experiments on a real-world dataset containing recordings of eye movements. In the future, the knowledge and transformation via the SAX method can be utilized to make sense of data and identify anomalies implemented in various domains and for multiple stakeholders.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology

A C126R de novo Mutation in CYBB Leads to X-linked Chronic Granulomatous Disease With Recurrent Pneumonia and BCGitis

Chronic granulomatous disease (CGD) is an innate immune deficiency of phagocytic cells caused by mutations that affect components of the NADPH oxidase system, with resulting impairment in reactive oxygen species production. Patients with CGD are susceptible to recurrent infections and hyperinflammatory responses. Mutations in CYBB lead to the X-linked form of CGD and are responsible for ~ 70% of cases. In this study, we report the case of a 2.5-year-old male patient with recurrent pneumonia and Bacillus Calmette-Guérin infection (BCGitis). As his first clinical manifestation, he presented with bullous impetigo at 18 days of age, which was followed by recurrent pneumonia and regional BCGitis. Genetic analysis revealed a de novo mutation in exon 5 of the CYBB gene: a single-nucleotide substitution, c.376T > C, leading to a C126R change

Analyzing time series from eye tracking using Symbolic Aggregate Approximation

This thesis explores the viability of transforming the data produced when tracking the eyes into a discrete symbolic representation. For this transformation, we utilize Symbolic Aggregate Approximation to investigate a new possibility for effectively categorizing data collected via eye tracking technologies. This categorization illustrates tendencies for, e.g., tracking problems, problems with the set-up, normal vision, or vision disturbances. Accordingly, this will contribute to evaluating the eyes' performance and allow professionals to develop a diagnosis based on evidence from objective measurements. The results are based on implementing a symbolic discretization method applied to experiments on a real-world dataset containing recordings of eye movements. In the future, the knowledge and transformation via the SAX method can be utilized to make sense of data and identify anomalies implemented in various domains and for multiple stakeholders

Hyperparameter optimization using Bayesian optimization on transfer learning for medical image classification

The field of medicine has a history of adopting new technology. Video equipment and sensors are used to visualize areas of interest in the human allowing for doctors to make diagnoses based on imagery observations. However, the detection rate of the doctors towards diseases and abnormalities is heavily dependent on the experience and state of mind of the doctor doing the examination. Computer-aided detection systems are systems designed to aid the doctor in improving the detection rate, and they are using or experimenting with machine learning. Deep convolutional neural networks, a type of machine learning, are shown to be highly efficient at image detection, classification, and analysis. However, these networks require large datasets to train properly. Transfer learning is a training technique where we use a pre-trained machine learning model and transfer some of the attained knowledge from other application domains over to a new model. This way, we can use small datasets and train a model in much shorter time. In this respect, transfer learning works fine but has many configurations called hyperparameters which are often not optimized. Our work aims to address the lack of automatic hyperparameter optimization for transfer learning by experiments utilizing a known hyperparameter optimization method and creating a system for running those experiments. First, we decided to focus on the field of gastroenterology by utilizing two publicly available datasets showing images from the gastrointestinal tract. Next, we used a specific transfer learning method and chose hyperparameters suitable for automatic optimization. The optimization method we chose was Bayesian optimization because of its reputation for being one of the best methods for hyperparameter optimization. However, Bayesian optimization has hyperparameters of its own, and there are also different versions of Bayesian optimization. We chose to limit the thesis, so we use standard Bayesian optimization with standard parameters. We created a system for running automatic experiments of three different hyperparameter optimization strategies. With the system, we ran a set of experiments for each dataset. Between the strategies, one was successful in achieving a high validation accuracy, while the others were considered failures. Compared to baselines, our best models was around 10% better. With these experiments, we demonstrated that automatic hyperparameter optimization is an effective strategy for increasing performance in transfer learning and that the best hyperparameters are nontrivial to select manually

The Kvasir-Capsule Dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. However, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. In this respect, we present Kvasir-Capsule, a large VCE dataset collected from examinations at Hospitals in Norway. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around detected anomalies from 14 different classes of findings. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. Initial work demonstrates the potential benefits ofAI-based computer-assisted diagnosis systems for VCE. However, they also show that there is great potential for improvements, and the Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order for VCE technology to reach its true potential

VISEM: A Multimodal Video Dataset of Human Spermatozoa

Real multimedia datasets that contain more than just images or text are rare. Even more so are open multimedia datasets in medicine. Often, clinically related datasets only consist of image or videos. In this paper, we present a dataset that is novel in two ways. Firstly, it is a multi-modal dataset containing different data sources such as videos, biological analysis data, and participant data. Secondly, it is the first dataset of that kind in the field of human reproduction. It consists of anonymized data from 85 different participants. We hope this dataset paper will inspire people to apply their knowledge in this important field, generate shareable results in the domain, and ultimately improve human infertility investigation and treatment

Flexible device compositions and dynamic resource sharing in PCIe interconnected clusters using Device Lending

Modern workloads often exceed the processing and I/O capabilities provided by resource virtualization, requiring direct access to the physical hardware in order to reduce latency and computing overhead. For computers interconnected in a cluser, access to remote hardware resources often requires facilitation both in hardware and specialized drivers with virtualization support. This limits the availability of resources to specific devices and drivers that are supported by the virtualization technology being used, as well as what the interconnection technology supports. For PCI Express (PCIe) clusters, we have previously proposed Device Lending as a solution for enabling direct low latency access to remote devices. The method has extremely low computing overhead, and does not require any application- or device-specific distribution mechanisms. Any PCIe device, such as network cards disks, and GPUs, can easily be shared among the connected hosts. In this work, we have extended our solution with support for a virtual machine (VM) hypervisor. Physical remote devices can be “passed through” to VM guests, enabling direct access to physical resources while still retaining the flexibility of virtualization. Additionally, we have also implemented multi-device support, enabling shortest-path peer-to-peer transfers between remote devices residing in different hosts.Our experimental results prove that multiple remote devices can be used, achieving bandwidth and latency close to native PCIe, and without requiring any additional support in device drivers. I/O intensive workloads run seamlessly using both local and remote resources. With our added VM and multi-device support, Device Lending offers highly customizable configurations of remote devices that can be dynamically reassigned and shared to optimize resource utilization, thus enabling a flexible composable I/O infrastructure for VMs as well as bare-metal machines