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Bilde av Møllersen, Kajsa
Bilde av Møllersen, Kajsa
Associate Professor, Biostatistics Department of Community Medicine kajsa.mollersen@uit.no +4777646197 You can find me here

Kajsa Møllersen

Job description

Research, teaching and outreach. For an overview of research interests, teaching and supervision responsibilities, see the banner Research and teaching.

  • Masoud Tafavvoghi, Lars Ailo Aslaksen Bongo, Nikita Shvetsov, Lill-Tove Rasmussen Busund, Kajsa Møllersen :
    Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review
    Journal of Pathology Informatics 2024 ARKIV / DOI
  • Tonje Kristin Jensen, Torbjørn Inge Tobiassen, Karsten Heia, Kajsa Møllersen, Roger B. Larsen, Margrethe Esaiassen :
    Effect of Codend Design and Postponed Bleeding on Hemoglobin in Cod Fillets Caught by Bottom Trawl in the Barents Sea Demersal Fishery
    Journal of Aquatic Food Product Technology 2022 ARKIV / DOI
  • Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen, Kajsa Møllersen, Lill-Tove Rasmussen Busund, Ruth Schwienbacher et al.:
    A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images
    Cancers 2022 ARKIV / DOI
  • Erica Ponzi, Magne Thoresen, Therese Haugdahl Nøst, Kajsa Møllersen :
    Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer
    BMC Bioinformatics 2021 ARKIV / DOI
  • Thomas Haugland Johansen, Steffen Aagaard Sørensen, Kajsa Møllersen, Fred Godtliebsen :
    Instance Segmentation of Microscopic Foraminifera
    Applied Sciences 2021 ARKIV / DOI
  • Ingvild Hersoug Nedberg, Marzia Lazzerini, Ilaria Mariani, Kajsa Møllersen, Emanuelle Pessa Valente, Erik Eik Anda et al.:
    Changes in maternal risk factors and their association with changes in cesarean sections in Norway between 1999 and 2016: A descriptive population-based registry study
    PLoS Medicine 2021 ARKIV / DOI
  • Bjørn Holdø, Kajsa Møllersen, Margareta Verelst, Ian Milsom, Rune Svenningsen, Finn Egil Skjeldestad :
    Surgeon’s experience and clinical outcome after retropubic tension‐free vaginal tape—A case series
    Acta Obstetricia et Gynecologica Scandinavica 2020 ARKIV / DOI
  • Kajsa Møllersen, Jon Yngve Hardeberg, Fred Godtliebsen :
    A probabilistic bag-to-class approach to multiple-instance learning
    Data 26. June 2020 ARKIV / DOI
  • Einar Holsbø, Kajsa Møllersen :
    Woes of The Practicing Omics Researchers
    Universitetsforlaget 2020
  • Thomas Haugland Johansen, Kajsa Møllersen, Samuel Ortega, Himar Fabelo, Aday Garcia, Gustavo Callico et al.:
    Recent advances in hyperspectral imaging for melanoma detection
    Wiley Interdisciplinary Reviews: Computational Statistics 2019 ARKIV / DOI
  • Mike Voets, Kajsa Møllersen, Lars Ailo Bongo :
    Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
    PLOS ONE 2019 ARKIV / DOI
  • Kajsa Møllersen, Maciel Zortea, Thomas Roger Griesbeck Schopf, Herbert M. Kirchesch, Fred Godtliebsen :
    Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images
    PLOS ONE 2017 ARKIV / DOI
  • Kajsa Møllersen, Subhra Dhar, Fred Godtliebsen :
    On Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering
    Applied Mathematics 2016 ARKIV / DOI
  • Kajsa Møllersen, Jon Yngve Hardeberg, Fred Godtliebsen :
    Divergence-based colour features for melanoma detection
    IEEE conference proceedings 2015 DOI
  • Kajsa Møllersen, Herbert M. Kirchesch, Maciel Zortea, Thomas Roger Griesbeck Schopf, Kristian Hindberg, Fred Godtliebsen :
    Computer-aided decision support for melanoma detection applied on melanocytic and non-melanocytic skin lesions: a comparison of two systems based on automatic analysis of dermoscopic images
    BioMed Research International 2015 ARKIV / DOI
  • Kajsa Møllersen, Maciel Zortea, Kristian Hindberg, Thomas Roger Griesbeck Schopf, Stein Olav Skrøvseth, Fred Godtliebsen :
    Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics
    CRC Press 2015 DOI
  • Maciel Zortea, Thomas Roger Griesbeck Schopf, Kevin Otto Thon, Marc Geilhufe, Kristian Hindberg, Herbert M. Kirchesch et al.:
    Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists
    Artificial Intelligence in Medicine 2014 FULLTEKST / DOI
  • Stein Olav Skrøvseth, Thomas Roger Griesbeck Schopf, Kevin Otto Thon, Maciel Zortea, Marc Geilhufe, Kajsa Møllersen et al.:
    A computer aided diagnostic system for malignant melanomas
    IEEE conference proceedings 2010 SAMMENDRAG
  • Kajsa Møllersen, Herbert M. Kirchesch, Thomas Roger Griesbeck Schopf, Fred Godtliebsen :
    Unsupervised segmentation for digital dermoscopic images
    Skin Research and Technology 2010 DOI
  • Mehrdad Rakaee, Masoud Tafavvoghi, Elio Adib, Biagio Ricciuti, Joao Alessi, Alessio Cortellini et al.:
    Artificial intelligence algorithm developed to predict immune checkpoint inhibitors efficacy in non–small-cell lung cancer
    2023
  • Kajsa Møllersen :
    The blind leading the blind? Filling the knowledge gap by student peer assessment
    2023 ARKIV
  • Kajsa Møllersen, Lars Ailo Bongo, Masoud Tafavvoghi :
    Cancer detection for white urban Americans
    2023 ARKIV
  • Kajsa Møllersen :
    Facilitating the spread of cancer (data) 
    2022
  • Kajsa Møllersen :
    The 99% accuracy club
    2022
  • Kajsa Møllersen :
    The 99% accuracy club
    2021 ARKIV
  • Kajsa Møllersen :
    Numbers, tables and statistics
    2021 ARKIV
  • Kajsa Møllersen :
    Pizza made healthy – an epidemiological cookbook
    2021 ARKIV
  • Thomas Haugland Johansen, Kajsa Møllersen, Samuel Ortega, Himar Fabelo, Gustavo Callico, Fred Godtliebsen :
    Detecting skin cancer using hyperspectral images
    Advanced Science News 2020
  • Kajsa Møllersen :
    Har du genet som gir brystkreft? Uten statistikkforskning får du aldri svar.
    www.forskning.no 2019
  • Kajsa Møllersen :
    Statistikkens estetikk
    2019
  • Kajsa Møllersen :
    Alle disse tallene, alle disse tabellene
    Tilfeldig gang 2019
  • Vibeke Os, Kajsa Møllersen :
    Nytt dataprogram for å oppdage føflekkreft
    Forskning.no 2016 FULLTEKST
  • Audun Hetland, Kajsa Møllersen :
    Do something meaningful
    2012

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

    Publications outside Cristin

    ”Databasert beslutningsstøtte for hudlesjoner ved mistanke om hudkreft”, Utposten (Norwegian journal for general practice and public health), 2017; 4, pp. 39-41

    Research interests

    My research revolves around statistical approaches to image analysis and machine learning. Specifically, I have published in topics of multiplicity, divergence functions, and multi-instance learning. I am currently engaged in three larger projects:
    Machine learning approaches to histopathological image analysis, which is a main focus of the Computational Pathology research group. The research group matches statistics and computer science with pathology and oncology. I jointly supervise three PhD students in this group.
    Statistical methods in ocean surveying, in the ICT+ project " Transforming ocean surveying by the power of DL and statistical methods".
    Integrative multi-omics analysis as part of the Systems Epidemiology research group. This includes the supervision of one PhD student.
    I engage in teaching, and research about teaching as a member of the research group Realfagsdidaktikk i høyere utdanning (Mathematics and Science Didactics in Higher Education), and various forms of outreach and dissemination.
    Other interests are gender diversity in STEM and data sharing.

    Applied statistics; Image analysis; Clustering; Divergence functions; Feature selection; Machine learning; Multiple instance learning; Melanoma; Breast cancer; Gene expression

    Teaching

    Research group: Realfagsdidaktikk i høyere utdanning  (leader Maarten Beerepoot)

    Publications and conferences:
    Møllersen K., & Coucheron, D. (2023, June 26). The blind leading the blind? Filling the knowledge gaps by student peer assessment. https://doi.org/10.35542/osf.io/vkjfe (preprint)
    Møllersen K, “Statistics without mathematics”, The 27th Nordic Conference in Mathematical Statistics, Tartu 2018

    Previous courses:
    2023 HEL-8047 Statistical models, PhD, Health Sciences, course leader and lecturer, UiT
    2023 ERN-1000/ODO-1004/MED-1501, Bachelor, Medical statistics, lecturer, UiT
    2022 HEL-8047 Statistical models, PhD, Health Sciences, course leader and lecturer, UiT
    2022 ERN-1000/ODO-1004/MED-1501, Bachelor, Medical statistics, lecturer, UiT
    2022 HEL-3070 Biostatistics, Master, Public health, course leader and lecturer, UiT
    2022 Special curriculum, PhD, Applied Linear Algebra, course leader, UiT
    2022 Theory of science, research ethics and methodology, Master, Midwife, lecturer, UiT
    2021 HEL-3006 Introduction to Biostatistics, Master of public health, Statistics, course leader and lecturer, UiT
    2021 Universitetspedagogikk, Pedagogical qualification, Student activity, lecturer, Nord University
    2020 Writer’s block and how to defeat it, Master, Nutrition, lecturer, UiT
    2011 The Elements of Statistical Learning II, PhD, Statistics, lecturer, UiT
    2010 The Elements of Statistical Learning I, PhD, Statistics, lecturer, UiT

     

    Researcher profile in Cristin
    Registrations in Munin
    ORCID: orcid.org/0000-0002-0619-7276

    Member of research group

    Systemepidemiologi   Norsk innhold Systems Epidemiology   English content Cultural Diversity in Mathematics Education (CuDiME)   English content Realfagsdidaktikk i høyere utdanning   Norsk innhold

    Member of project

    Transforming ocean surveying by the power of DL and statistical methods    English content