• Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy 

      Wickstrøm, Kristoffer; Løkse, Sigurd Eivindson; Kampffmeyer, Michael; Yu, Shujian; Príncipe, José C.; Jenssen, Robert (Journal article; Peer reviewed, 2023)
      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 ...
    • A clinically motivated self-supervised approach for content-based image retrieval of CT liver images 

      Wickstrøm, Kristoffer; Østmo, Eirik Agnalt; Radiya, Keyur; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2023-05-09)
      Deep learning-based approaches for content-based image retrieval (CBIR)of computed tomography (CT) liver images is an active field of research but suffer from some critical limitations. First,they are heavily reliant on ...
    • Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings 

      Trosten, Daniel Johansen; Chakraborty, Rwiddhi; Løkse, Sigurd Eivindson; Wickstrøm, Kristoffer; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2023)
      Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where ...
    • The Kernelized Taylor Diagram 

      Wickstrøm, Kristoffer; Johnson, Juan Emmanuel; Løkse, Sigurd Eivindson; Camps-Valls, Gusatu; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2022)
      This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to ...
    • Mixing up contrastive learning: Self-supervised representation learning for time series 

      Wickstrøm, Kristoffer; Kampffmeyer, Michael; Mikalsen, Karl Øyvind; Jenssen, Robert (Journal article; Peer reviewed, 2022)
    • View it like a radiologist: Shifted windows for deep learning augmentation of CT images 

      Østmo, Eirik Agnalt; Wickstrøm, Kristoffer; Radiya, Keyur; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2023)
      Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in medical images. However, most deep learning ...