Michael Kampffmeyer


Vuosttašamanueansa

Virgečilgehus

Member of the UiT Machine Learning Group.

Personal website


  • Duy Khoi Tran, van Nhan Nguyen, Davide Roverso, Robert Jenssen, Michael Christian Kampffmeyer :
    LSNetv2: Improving weakly supervised power line detection with bipartite matching
    Expert Systems With Applications 2024 DOI
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Robert Jenssen, Michael Christian Kampffmeyer :
    Leveraging tensor kernels to reduce objective function mismatch in deep clustering
    Pattern Recognition 2024 DOI
  • Samuel Kuttner, Luigi Tommaso Luppino, Laurence Convert, Otman Sarrhini, Roger Lecomte, Michael Christian Kampffmeyer et al.:
    Deep learning derived input function in dynamic [18F]FDG PET imaging of mice
    Frontiers in Nuclear Medicine 2024 DOI
  • Stine Hansen, Srishti Gautam, Suaiba Amina Salahuddin, Michael Christian Kampffmeyer, Robert Jenssen :
    ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement
    Medical Image Analysis 2023 ARKIV / DOI
  • Eirik Agnalt Østmo, Kristoffer Wickstrøm, Keyur Radiya, Michael Kampffmeyer, Robert Jenssen :
    View it like a radiologist: Shifted windows for deep learning augmentation of CT images
    Machine Learning for Signal Processing 2023 ARKIV / DOI
  • Haoyuan Li, Haoye Dong, Hanchao Jia, Dong Huang, Michael Christian Kampffmeyer, Liang Lin et al.:
    Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos
    IEEE International Conference on Computer Vision (ICCV) 2023 ARKIV
  • Xujie Zhang, Binbin Yang, Michael Christian Kampffmeyer, Wenqing Zhang, Shiyue Zhang, Guansong Lu et al.:
    DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
    IEEE International Conference on Computer Vision (ICCV) 2023 ARKIV
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
    Computer Vision and Pattern Recognition 22. Aug 2023 ARKIV / DATA / DOI
  • Kristoffer Wickstrøm, Sigurd Eivindson Løkse, Michael Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen :
    Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
    Entropy 2023 ARKIV / DOI
  • Durgesh Kumar Singh, Ahcene Boubekki, Robert Jenssen, Michael Kampffmeyer :
    Supercm: Revisiting Clustering for Semi-Supervised Learning
    Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Ahcene Boubekki, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    RELAX: Representation Learning Explainability
    International Journal of Computer Vision 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
    Computerized Medical Imaging and Graphics 2023 ARKIV / DOI
  • Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Robert Jenssen :
    Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
    IEEE Journal of Oceanic Engineering 2023 ARKIV / DOI
  • Daniel Johansen Trosten, Rwiddhi Chakraborty, Sigurd Eivindson Løkse, Kristoffer Wickstrøm, Robert Jenssen, Michael Kampffmeyer :
    Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
    Computer Vision and Pattern Recognition 2023 ARKIV / DOI
  • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing :
    Federated Partially Supervised Learning With Limited Decentralized Medical Images
    IEEE Transactions on Medical Imaging 2023 ARKIV / DOI
  • Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Christian Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke :
    Automatic identification of chemical moieties
    Physical Chemistry, Chemical Physics - PCCP 2023 ARKIV / DOI
  • Rogelio Andrade Mancisidor, Michael Christian Kampffmeyer, Kjersti Aas, Robert Jenssen :
    Discriminative multimodal learning via conditional priors in generative models
    Neural Networks 2023 ARKIV / DOI
  • Kristoffer Vinther Olesen, Ahcene Boubekki, Michael Christian Kampffmeyer, Robert Jenssen, Anders Nymark Christensen, Sune Hørlück et al.:
    A Contextually Supported Abnormality Detector for Maritime Trajectories
    Journal of Marine Science and Engineering (JMSE) 2023 ARKIV / DOI
  • Luca Tomasetti, Stine Hansen, Mahdieh Khanmohammadi, Kjersti Engan, Liv Jorunn Høllesli, Kathinka Dæhli Kurz et al.:
    Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
    IEEE International Symposium on Biomedical Imaging 2023 ARKIV / DOI
  • Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, Eric Xing :
    Towards robust partially supervised multi-structure medical image segmentation on small-scale data
    Applied Soft Computing 2022 ARKIV / DOI
  • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing :
    Negational symmetry of quantum neural networks for binary pattern classification
    Pattern Recognition 2022 ARKIV / DOI
  • Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer :
    Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels
    Medical Image Analysis 2022 ARKIV / DOI
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    Communications in Computer and Information Science (CCIS) 2022 ARKIV / DOI
  • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu :
    Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
    Lecture Notes in Computer Science (LNCS) 2022 DOI
  • Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael Kampffmeyer, Xiaoyong Wei et al.:
    M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
    Computer Vision and Pattern Recognition 2022 DOI
  • Zaiyu Huang, Hanhui Li, Zhenyu Xie, Michael Kampffmeyer, Qingling Cai, Xiaodan Liang :
    Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning
    Advances in Neural Information Processing Systems 2022 DOI
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    IEEE International Symposium on Biomedical Imaging 2022 DOI
  • Ingeborg Mathiesen, Theodor Anton Ross, Anna Kaarina Pöntinen, Einar Holsbø, Michael Kampffmeyer, Mona Johannessen et al.:
    Characterization of Putative Virulence Factors in Enterococcus faecium
    2023
  • Magnus Oterhals Størdal, Benjamin Ricaud, Michael Christian Kampffmeyer, Geir Bertelsen, Maja Gran Erke :
    Risk Prediction of Diabetic Retinopathy in the Tromsø Study
    2023
  • Fredrik Emil Aspheim, Samuel Kuttner, Luigi Tommaso Luppino, Rune Sundset, Michael Christian Kampffmeyer, Robert Jenssen :
    Deep learning derived input-function in dynamic PET-imaging
    2023
  • Michael Christian Kampffmeyer :
    Learning from limited labeled data for few-shot medical image segmentation (and beyond)
    2023
  • Michael Christian Kampffmeyer :
    Deep Clustering
    2023
  • Michael Christian Kampffmeyer :
    UiT Machine Learning Group
    2023
  • Michael Christian Kampffmeyer :
    Deep Multi-view Clustering
    2023
  • Michael Christian Kampffmeyer :
    AI’S FUTURE PATH, WHAT ARE THE OPPORTUNITIES?
    2023
  • Michael Christian Kampffmeyer :
    Self-Explainable Deep Learning
    2023
  • Michael Christian Kampffmeyer :
    Hva er kunstig intelligens (KI)? Muligheter og utfordringer
    2023
  • Michael Christian Kampffmeyer :
    Learning from limited labelled data for medical image segmentation
    2023
  • Magnus Oterhals Størdal, Benjamin Ricaud, Michael Christian Kampffmeyer, Geir Bertelsen, Maja Gran Erke :
    Risk Prediction of Diabetic Retinopathy in the Tromsø Study
    2023
  • Arnt-Børre Salberg, Michael Christian Kampffmeyer :
    Trends in deep learning
    2023
  • Fredrik Emil Aspheim, Luigi Tommaso Luppino, Michael Christian Kampffmeyer, Robert Jenssen, Rune Sundset, Akos Samuel Kuttner :
    Interpretable deep learning model for input function estimation in small-animal 18F-FDG PET imaging
    2023
  • Changkyu Choi, Michael Christian Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Robert Jenssen :
    Deep Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
    2023
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Kristoffer Knutsen Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Daniel Johansen Trosten, Kristoffer Wickstrøm, Shujian Yu, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    Deep Clustering with the Cauchy-Schwarz Divergence
    2022
  • Changkyu Choi, Shujian Yu, Michael Kampffmeyer, Arnt-Børre Salberg, Nils Olav Handegard, Suaiba Amina Salahuddin et al.:
    Explaining Marine Acoustic Target Classification in Multi-channel Echosounder Data using Self-attention Mask, Information-Bottleneck, and Mask Prior
    2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    2022
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    2022
  • Theodor Anton Ross, Anna Kaarina Pöntinen, Jessin Janice, Einar Holsbø, Jukka Corander, Kristin Hegstad et al.:
    Leveraging machine learning for finding novel putative virulence factors in Enterococcus faecium
    2022
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Explaining representations for medical image retrieval
    2022

  • The 50 latest publications is shown on this page. See all publications in Cristin here →



    Member of research group