Kajsa Møllersen

According to our database, Kajsa Møllersen authored at least 11 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of five.

2023

Publicly available datasets of breast histopathology H&E whole-slide images: A systematic review.

Masoud Tafavvoghi, Lars Ailo Bongo, Nikita Shvetsov, Lill-Tove Rasmussen Busund, Kajsa Møllersen

CoRR, 2023

What is the state of the art? Accounting for multiplicity in machine learning benchmark performance.

Kajsa Møllersen, Einar J. Holsbø

CoRR, 2023

2022

A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images.

Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen, Kajsa Møllersen, Lill-Tove Rasmussen Busund, Ruth Schwienbacher, Lars Ailo Bongo, Thomas K. Kilvaer

CoRR, 2022

2021

Instance Segmentation of Microscopic Foraminifera.

Thomas Haugland Johansen, Steffen Aagaard Sørensen, Kajsa Møllersen, Fred Godtliebsen

CoRR, 2021

Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer.

Erica Ponzi, Magne Thoresen, Therese Haugdahl Nøst, Kajsa Møllersen

BMC Bioinform., 2021

2020

A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning.

Kajsa Møllersen, Jon Yngve Hardeberg, Fred Godtliebsen

Data, 2020

2018

Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

Mike Voets, Kajsa Møllersen, Lars Ailo Bongo

CoRR, 2018

A bag-to-class divergence approach to multiple-instance learning.

Kajsa Møllersen, Jon Yngve Hardeberg, Fred Godtliebsen

CoRR, 2018

2016

On Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering.

Kajsa Møllersen, Subhra S. Dhar, Fred Godtliebsen

CoRR, 2016

2015

Divergence-based colour features for melanoma detection.

Kajsa Møllersen, Jon Yngve Hardeberg, Fred Godtliebsen

Proceedings of the Colour and Visual Computing Symposium, 2015

2014

Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists.

Maciel Zortea, Thomas R. Schopf, Kevin Thon, Marc Geilhufe, Kristian Hindberg, Herbert M. Kirchesch, Kajsa Møllersen, Jörn Schulz, Stein Olav Skrøvseth, Fred Godtliebsen

Artif. Intell. Medicine, 2014