Oliver Tomic

Norwegian University of Life Sciences (NMBU) · Department of Mathematical Sciences and Technology (IMT)

Objective. Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and consistent but may produce unrealistic contours or miss relevant structures. We evaluate approaches for increasing the...

This literature review assesses the efficacy of image-processing techniques and machine-learning models in computer vision for wood log grading and scaling. Four searches were conducted in four scientific databases, yielding a total of 1288 results, which were narrowed down to 33 relevant studies. The studies were categorized according to their goa...

[This corrects the article DOI: 10.3389/fmed.2023.1217037.].

Background Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simp...

Background: Artificial neural networks (ANNs) can be a powerful tool for spectroscopic data analysis. Their ability to detect and model complex relations in the data may lead to outstanding predictive capabilities, but the predictions themselves are difficult to interpret due to the lack of understanding of the black box ANN models. ANNs and linea...

Background Radiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose t...

Determining the most informative features for predicting the overall survival of patients diagnosed with high-grade gastroenteropancreatic neuroendocrine neoplasms is crucial to improve individual treatment plans for patients, as well as the biological understanding of the disease. Recently developed ensemble feature selectors like the Repeated Ela...

In artificial neural networks, understanding the contributions of input features on the prediction fosters model explainability and delivers relevant information about the dataset. While typical setups for feature importance ranking assess input features individually, in this study, we go one step further and rank the importance of groups of featur...

Feature selection reduces the complexity of high-dimensional datasets and helps to gain insights into systematic variation in the data. These aspects are essential in domains that rely on model interpretability, such as life sciences. We propose a (U)ser-Guided (Bay)esian Framework for (F)eature (S)election, UBayFS, an ensemble feature selection te...

In artificial neural networks, understanding the contributions of input features on the prediction fosters model explainability and delivers relevant information about the dataset. While typical setups for feature importance ranking assess input features individually, in this study, we go one step further and rank the importance of groups of featur...

Prediction of cancer treatment outcomes based on baseline patient characteristics is a challenging but necessary step towards more personalized treatments with the aim of increased survival and quality of life. The HEad and neCK TumOR Segmentation Challenge (HECKTOR) 2021 comprises two major tasks: auto-segmentation of GTVt in FDG-PET/CT images and...

Auto-segmentation of head and neck cancer (HNC) primary gross tumor volume (GTVt) is a necessary but challenging process for radiotherapy treatment planning and radiomics studies. The HEad and neCK TumOR Segmentation Challenge (HECKTOR) 2021 comprises two major tasks: auto-segmentation of GTVt in FDG-PET/CT images and the prediction of patient outc...

Low lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed using density functional theory (DFT), typically at a high computational cost. Training machine learning models to predict lattice thermal conductivity could offer an effective procedure to identify low lat...

ConsumerCheck is an open source data analysis software tailored for analysis of sensory and consumer data. Since some of the implemented methods are generic, such as PCA, PLSR and PCR, other data from other domains may also be analysed with ConsumerCheck. The software comes with a graphical user interface and as such provides non-statisticians and...

Background Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time and increase delineation consistency. In this study, the applicability of deep learning for fully autom...

Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study, we present the Repeated Elastic Net Technique (REN...

Preprocessing is a mandatory step in most types of spectroscopy and spectrometry. The choice of preprocessing method depends on the data being analysed, and to get the preprocessing right, domain knowledge or trial and error is required. Given the recent success of deep learning-based methods in numerous applications and their ability to automatica...

Purpose: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV from FDG-PET/CT images of patients with head and neck cancer (HNC). CNN models were compared to ma...

Low lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed based on density functional theory calculations, but such calculations carry a high computational cost and machine learning is therefore increasingly being used to estimate lattice thermal conductivity at a...

This article presents a discussion of principal components analysis of descriptive sensory data. Focus is on standardization, many correlated variables, validation, and the use of descriptive data in preference mapping. Different ways of performing the analysis are presented and discussed with focus on how to obtain informative and reliable results...

Training predictive models on high-dimensional datasets is a challenging task in artificial intelligence. Users must take measures to prevent overfitting and keep model complexity low. Thus, the feature selection plays a key role in data preprocessing and delivers insights into the systematic variation in the data. The latter aspect is crucial in d...

Target volume delineation is a vital but time-consuming and challenging part of radiotherapy, where the goal is to deliver sufficient dose to the target while reducing risks of side effects. For head and neck cancer (HNC) this is complicated by the complex anatomy of the head and neck region and the proximity of target volumes to organs at risk. Th...

RENT is a feature selection method for binary classification and regression problems. At its core, RENT trains an ensemble of unique models using regularized elastic net to select features. Each model in the ensemble is trained with a unique and randomly selected subset from the full training data. From these models one can acquire weight distribut...

The study determined optimal parameters to four preprocessing techniques for mid-infrared (MIR) spectra of wines and grape berry homogenates and tested MIR’s ability to model sensory properties of research Cabernet Sauvignon and Chardonnay wines. Savitsky-Golay (SG) derivative, smoothing points, and polynomial order, and extended multiplicative sig...

In this study we present the RENT feature selection method for binary classification and regression problems. We compare the performance of RENT to a number of other state-of-the-art feature selection methods on eight datasets (six for binary classification and two for regression) to illustrate RENT's performance with regard to prediction and reduc...

This paper is about the use of the multiblock regression method sequential and orthogonalized partial least squares (SO‐PLS) for path modeling. The paper is a follow up of previously published papers on the same topic and presents a number of new results for the method. First of all, the paper discusses more thoroughly the aspect of how to incorpor...

In an investigation of objective measures that link grape composition to wine quality, this study sought to identify Cabernet Sauvignon grape parameters that predict the sensory properties of the corresponding wines. Eleven chemical measures comprising volatile and non-volatile compounds, enzyme activity plus standard industry harvest measurements...

In the development of sensory and consumer science, data are often collected in several blocks responding to different aspects of consumer experience. Sometimes the task of organizing the data and explaining their relation is non-trivial, especially when considering structural (casual) relationship between data sets. In this sense, PLS path modelli...

Growing health concerns have increased interest in reducing the consumption of added sugars, which can be achieved by substituting or replacing sugar with sweeteners to maintain sensory intensity and quality. The growing availability of sweeteners has increased the complexity of the perceptual landscape as sweeteners differ in the qualitative, inte...

An automatic segmentation algorithm for delineation of the gross tumour volume and pathologic lymph nodes of head and neck cancers in PET/CT images is described. The proposed algorithm is based on a convolutional neural network using the U-Net architecture. Several model hyperparameters were explored and the model performance in terms of the Dice s...

Biochar has been shown to reduce nitrous oxide (N 2 O)emissions from soils, but the effect is highly variable across studies and the mechanisms are under debate. To improve our mechanistic understanding of biochar effects on N 2 O emission, we monitored kinetics of NO, N 2 O and N 2 accumulation in anoxic slurries of a peat and a mineral soil, spik...

Objectives Postoperative wound dehiscence (PWD) is a serious complication to laparotomy, leading to higher mortality, readmissions and cost. The aims of the present study are to investigate whether risk adjusted PWD rates could reliably differentiate between Norwegian hospitals, and whether PWD rates were associated with hospital characteristics su...

An increase in consumer awareness around the negative health impacts of consuming excess added sugars has led to a rise in the replacement of sucrose in foods and beverages. This replacement is often through the use of low or no calorie sweeteners to reduce total calories while maintaining sweetness and palatability. There are a wide variety of swe...

This paper presents a new approach to path modeling named SO‐PLS path modeling (SO‐PLS‐PM) and compares it with the more well‐known PLS path modeling (PLS‐PM). The new method is flexible and graphically oriented and allows for handling multidimensional blocks and diagnosing missing paths. In order to allow for a thorough comparison between the two...

The current study determined the applicability of sequential and orthogonalised-partial least squares (SO-PLS) regression to relate Cabernet Sauvignon grape chemical composition to the sensory perception of the corresponding wines. Grape samples (n = 25) were harvested at a similar maturity and vinified identically in 2013. Twelve measures using va...

«Health at a Glance» is a series of reports published by the OECD every other/second year/biannually. The reports present the most recent comparable data on the health status of populations and health system performance in OECD countries. The 2017 edition contains results from 35 OECD countries, based on the latest available data from 2015 and 2016...

https://www.fhi.no/publ/2016/helse-i-norge-2016/

Background: The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Minis...

Time series plots are widely used, across sectors and media, probably because many find them easy to understand. Figure 1 is a time series plot of how the readmission rate in a hospital changed over time (constructed data set). Figure 1 Example of fictive time series data displaying the percentage of discharged patients readmitted to the hospital...

This paper discusses the use of the new path modelling approach based on Sequential Orthogonalised PLS regression within the context of consumer science. The method is based on splitting the estimation process into a sequence of modelling steps for each dependent block versus its predictive blocks. Focus will be on how the method can be used to com...