Blar i forfatter "Fabelo, Himar"

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    • Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases 

      García-Vicente, Clara; Chushig-Muzo, David; Mora-Jimenez, Inmaculada; Fabelo, Himar; Gram, Inger Torhild; Løchen, Maja-Lisa; Granja, Conceição; Soguero-Ruiz, Cristina (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-01-21)
      Noncommunicable diseases are among the most significant health threats in our society, being cardiovascular diseases (CVD) the most prevalent. Because of the severity and prevalence of these illnesses, early detection and prevention are critical for reducing the worldwide health and economic burden. Though machine learning (ML) methods usually outperform conventional approaches in many domains, class ...

    • Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images 

      Uteng, Stig; Johansen, Thomas Haugland; Zaballos, Jose Ignacio; Ortega, Samuel; Holmström, Lasse; Callico, Gustavo M.; Fabelo, Himar; Godtliebsen, Fred (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-03-27)
      Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples include identification of potentially malignant changes in skin moles or the gradual onset of food quality deterioration. Statistical scale-space methodologies ...

    • Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors 

      García-Vicente, Clara; Chushig-Muzo, David; Mora-Jiménez, Inmaculada; Fabelo, Himar; Gram, Inger Torhild; Løchen, Maja-Lisa; Granja, Conceição; Soguero-Ruiz, Cristina (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-23)
      Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the ...

    • Recent advances in hyperspectral imaging for melanoma detection 

      Johansen, Thomas Haugland; Møllersen, Kajsa; Ortega, Samuel; Fabelo, Himar; Garcia, Aday; Callico, Gustavo; Godtliebsen, Fred (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-04-22)
      Skin cancer is one of the most common types of cancer. Skin cancers are classified as nonmelanoma and melanoma, with the first type being the most frequent and the second type being the most deadly. The key to effective treatment of skin cancer is early detection. With the recent increase of computational power, the number of algorithms to detect and classify skin lesions has increased. The overall ...