Cecilia Marie Futsæther

Exploratory immunomonitoring during radiochemotherapy in HNSCC and machine-learning reveal immune parameters associated with disease-free survival

Anna-Jasmina Donaubauer, Oliver Tomić, Lia Mogge, Sarina K. Müller et autres

Immunological biomarkers are increasingly relevant for personalized cancer treatment, but peripheral blood-derived biomarkers are not yet used to guide therapy in head and neck squamous cell carcinoma (HNSCC). The prospective non-randomized DIREKHT study (ClinicalTrials.gov: NCT02528955, 2015-08-19) therefore integrated immune monitoring into postoperative …

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CNN-based prediction using early post-radiotherapy MRI as a proxy for toxicity in the murine head and neck

Bao Ngoc Huynh, Manish Kakar, Olga Zlygosteva, Inga Solgård Juvkam et autres

BACKGROUND AND PURPOSE: Radiotherapy (RT) of head and neck cancer can cause severe toxicities. Early identification of individuals at risk could enable personalized treatment. This study evaluated whether convolutional neural networks (CNNs) applied to Magnetic Resonance (MR) images acquired early after irradiation …

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3728 Machine and deep learning time-to-event models with explainability for head and neck cancer outcome predictions

Bao Ngoc Huynh, T. Moen, Aurora Rosvoll Groendahl, Ingerid Skjei Knudtsen et autres

Attention-based Vision Transformer Enables Early Detection of Radiotherapy-Induced Toxicity in Magnetic Resonance Images of a Preclinical Model

Manish Kakar, Bao Ngoc Huynh, Olga Zlygosteva, Inga Solgård Juvkam et autres

IntroductionEarly identification of patients at risk for toxicity induced by radiotherapy (RT) is essential for developing personalized treatments and mitigation plans. Preclinical models with relevant endpoints are critical for systematic evaluation of normal tissue responses. This study aims to determine whether attention-based …

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Deep learning can detect elbow disease in dogs screened for elbow dysplasia

Mari Nyborg Hauback, Bao Ngoc Huynh, Sunniva Elisabeth Daae Steiro, Aurora Rosvoll Groendahl et autres

Medical image analysis based on deep learning is a rapidly advancing field in veterinary diagnostics. The aim of this retrospective diagnostic accuracy study was to develop and assess a convolutional neural network (CNN, EfficientNet) to evaluate elbow radiographs from dogs screened for …

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Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation

Bao Ngoc Huynh, Aurora Rosvoll Groendahl, Oliver Tomić, Kristian Hovde Liland et autres

Abstract 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. …

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A Comparative Literature Review of Machine Learning and Image Processing Techniques Used for Scaling and Grading of Wood Logs

Yohann Jacob Sandvik, Cecilia Marie Futsæther, Kristian Hovde Liland, Oliver Tomić

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 …

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Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics

Bao Ngoc Huynh, Aurora Rosvoll Groendahl, Oliver Tomić, Kristian Hovde Liland et autres

Corrigendum on: Huynh BN, Groendahl AR, Tomic O, Liland KH, Knudtsen IS, Hoebers F, van Elmpt W, Malinen E, Dale E and Futsaether CM (2023) Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and …

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966: Uncertainty in deep learning-based automatic segmentations of head and neck cancer tumors

Bao Ngoc Huynh, Aurora Rosvoll Groendahl, Oliver Tomić, Ingerid Skjei Knudtsen et autres

Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics

Bao Ngoc Huynh, Aurora Rosvoll Groendahl, Oliver Tomić, Kristian Hovde Liland et autres

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. …

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Tsetlin Machine in DNA sequence classification : Application to prokaryote gene prediction / A match made in silico

Kristian Hovde Liland, Oliver Tomić, Ulf Geir Indahl, Cecilia Marie Futsæther et autres

The Tsetlin machine (TM) is a logic-based machine learning model with the crucial advantages of transparency and hardware-friendliness. In TM, groups of Tsetlin Automata (TAs) produce Boolean expressions in the form of conjunctive clauses in AND-rules. In this work, we show that …

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Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures

Lorenzo Fongaro, Cecilia Marie Futsæther, Oliver Tomić, Isak B. Lande et autres

Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at …

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