Kajsa Møllersen
Kajsa Møllersen
Associate professor, Biostatistics, UiT- The Arctic University of Norway
Verified email at - Homepage
Cited by
Cited by
Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
M Voets, K Møllersen, LA Bongo
PloS one 14 (6), e0217541, 2019
Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists
M Zortea, TR Schopf, K Thon, M Geilhufe, K Hindberg, H Kirchesch, ...
Artificial intelligence in medicine 60 (1), 13-26, 2014
Recent advances in hyperspectral imaging for melanoma detection
TH Johansen, K Møllersen, S Ortega, H Fabelo, A Garcia, GM Callico, ...
Wiley Interdisciplinary Reviews: Computational Statistics 12 (1), e1465, 2020
Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
M Voets, K Møllersen, LA Bongo
arXiv preprint arXiv:1803.04337, 2018
Unsupervised segmentation for digital dermoscopic images
K Møllersen, HM Kirchesch, TG Schopf, F Godtliebsen
Skin Research and Technology 16 (4), 401-407, 2010
A computer aided diagnostic system for malignant melanomas
SO Skrøvseth, TR Schopf, K Thon, M Zortea, M Geilhufe, K Møllersen, ...
2010 3rd International Symposium on Applied Sciences in Biomedical and …, 2010
Computer-aided decision support for melanoma detection applied on melanocytic and nonmelanocytic skin lesions: a comparison of two systems based on automatic analysis of …
K Møllersen, H Kirchesch, M Zortea, TR Schopf, K Hindberg, ...
BioMed research international 2015, 2015
Divergence-based colour features for melanoma detection
K Møllersen, JY Hardeberg, F Godtliebsen
2015 Colour and Visual Computing Symposium (CVCS), 1-6, 2015
Improved skin lesion diagnostics for general practice by computer-aided diagnostics
K Møllersen, M Zortea, K Hindberg, TR Schopf, SO Skrøvseth, ...
Dermoscopy image analysis, 257-302, 2015
A pragmatic machine learning approach to quantify Tumor-Infiltrating lymphocytes in whole slide images
N Shvetsov, M Grønnesby, E Pedersen, K Møllersen, LTR Busund, ...
Cancers 14 (12), 2974, 2022
Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images
K Møllersen, M Zortea, TR Schopf, H Kirchesch, F Godtliebsen
PloS one 12 (12), e0190112, 2017
On Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering
K Møllersen, SS Dhar, F Godtliebsen
arXiv preprint arXiv:1609.06533, 2016
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
IH Nedberg, M Lazzerini, I Mariani, K Møllersen, EP Valente, EE Anda, ...
PLoS Medicine 18 (9), e1003764, 2021
Surgeon’s experience and clinical outcome after retropubic tension‐free vaginal tape—A case series
B Holdø, K Møllersen, M Verelst, I Milsom, R Svenningsen, ...
Acta Obstetricia et Gynecologica Scandinavica, 2020
Unsupervised segmentation of skin lesions
K Møllersen
Universitetet i Tromsø, 2008
A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning
K Møllersen, JY Hardeberg, F Godtliebsen
Data 5 (2), 56, 2020
A bag-to-class divergence approach to multiple-instance learning
K Møllersen, JY Hardeberg, F Godtliebsen
arXiv preprint arXiv:1803.02782, 2018
Effect of codend design and postponed bleeding on hemoglobin in cod fillets caught by bottom trawl in the Barents Sea demersal fishery
TK Jensen, T Tobiassen, K Heia, K Møllersen, RB Larsen, M Esaiassen
Journal of Aquatic Food Product Technology 31 (8), 775-784, 2022
Melanoma detection Colour, clustering and classification
K Møllersen
UiT Norges arktiske universitet, 2016
What is the state of the art? Accounting for multiplicity in machine learning benchmark performance
K Møllersen, H Einar
arXiv, 2023
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