| 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 | 74 | 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 | 68 | 2014 |
| 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 | 41 | 2018 |
| 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 | 33 | 2020 |
| Unsupervised segmentation for digital dermoscopic images K Møllersen, HM Kirchesch, TG Schopf, F Godtliebsen Skin Research and Technology 16 (4), 401-407, 2010 | 29 | 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 | 26 | 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 | 19 | 2015 |
| Divergence-based colour features for melanoma detection K Møllersen, JY Hardeberg, F Godtliebsen 2015 Colour and Visual Computing Symposium (CVCS), 1-6, 2015 | 14 | 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 | 13* | 2015 |
| 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 | 9 | 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 | 5 | 2016 |
| 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 | 3 | 2020 |
| Unsupervised segmentation of skin lesions K Møllersen Universitetet i Tromsø, 2008 | 3 | 2008 |
| A bag-to-class divergence approach to multiple-instance learning K Møllersen, JY Hardeberg, F Godtliebsen arXiv preprint arXiv:1803.02782, 2018 | 2 | 2018 |
| Melanoma detection Colour, clustering and classification K Møllersen UiT Norges arktiske universitet, 2016 | 1 | 2016 |
| A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning K Møllersen, JY Hardeberg, F Godtliebsen Data 5 (2), 56, 2020 | | 2020 |
| Integrative analyses of multi-omics data improves model predictions: an application to lung cancer E Ponzi, M Thoresen, TH Nøst, K Møllersen bioRxiv, 2020 | | 2020 |
| 5. Woes of The Practicing Omics Researcher E Holsbø, K Møllersen Advancing Systems Epidemiology in Cancer, 77-94, 0 | | |
