English
norskBlar i forfatter "Wickstrøm, Kristoffer Knutsen"
Viser treff 1-11 av 11
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Advancing Deep Learning with Emphasis on Data-Driven Healthcare
(Doctoral thesis; Doktorgradsavhandling, 2022-10-28)Retten til helse er en grunnleggende menneskerettighet, men mange utfordringer står overfor de som ønsker å etterleve denne retten. Mangel på utdannet helsepersonell, økte kostnader og en aldrende befolkning er bare noen få eksempler på nåværende hindringer i helsesektoren. Å takle slike problemer er avgjørende for å gi pålitelig helsehjelp med høy kvalitet til mennesker over hele verden. Mange ... -
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-06-03)Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs’ generalization ability. However, it is by no means obvious how to estimate the mutual information (MI) between each hidden layer and the input/desired output to construct the IP. For instance, hidden layers with many neurons require MI estimators ... -
Auroral Image Classification With Deep Neural Networks
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-05)Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere‐magnetosphere environment. Automatic classification of <i>millions</i> of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statistics and for supporting scientists to study ... -
Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-08)<p>Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of <sup>15</sup>O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning ... -
Machine learning derived input-function in a dynamic 18F-FDG PET study of mice
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-01-13)Tracer kinetic modelling, based on dynamic <sup>18</sup>F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. Our aim was to explore two non-invasive machine learning-based models, for AIF prediction in a small-animal dynamic FDG PET study. 7 tissue ... -
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
(Journal article; Tidsskriftartikkel, 2023)Explainable AI (XAI) is a rapidly evolving field that aims to improve transparency and trustworthiness of AI systems to humans. One of the unsolved challenges in XAI is estimating the performance of these explanation methods for neural networks, which has resulted in numerous competing metrics with little to no indication of which one is to be preferred. In this paper, to identify the most reliable ... -
Selective Imputation for Multivariate Time Series Datasets with Missing Values
(Journal article; Tidsskriftartikkel, 2023-01-31)Multivariate time series often contain missing values for reasons such as failures in data collection mechanisms. Since these missing values can complicate the analysis of time series data, imputation techniques are typically used to deal with this issue. However, the quality of the imputation directly affects the performance of downstream tasks. In this paper, we propose a selective imputation ... -
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11-20)Colorectal polyps are known to be potential precursors to colorectal cancer, which is one of the leading causes of cancer-related deaths on a global scale. Early detection and prevention of colorectal cancer is primarily enabled through manual screenings, where the intestines of a patient is visually examined. Such a procedure can be challenging and exhausting for the person performing the screening. ... -
Uncertainty modeling and interpretability in convolutional neural networks for polyp segmentation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-11-01)Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well ... -
Uncertainty Modeling and Interpretability in Convolutional Neural Networks for Polyp Segmentation.
(Master thesis; Mastergradsoppgave, 2018-05-14)Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, with prevention commonly done through regular colonoscopy screenings. During a colonoscopy, physicians manually inspect the colon of a patient using a camera in search for polyps, which are known to be possible precursors to colorectal cancer. Seeing that a colonoscopy is a manual procedure, it can be susceptible to ... -
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-12-07)Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what influenced the predictions, which is critical for clinical tasks. However, existing explainability techniques lack an important component for trustworthy ...
