108 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- UiT The Arctic University of NorwayNO10 papers
- Centre for Arctic Gas Hydrate, Environment and ClimateNO2 papers
- Norwegian Computing CenterNO2 papers
- Indian Institute of Technology KanpurIN1 paper
- Sapienza University of RomeIT1 paper
- Università della Svizzera italianaCH1 paper
- University of CopenhagenDK1 paper
10 papers
A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images
Nikita Shvetsov, Thomas K. Kilvaer, Masoud Tafavvoghi +4
Developing clinically useful cell-level analysis tools in digital pathology remains challenging due to limitations in dataset granularity, inconsistent annotations, high computatio…
Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer
Nikita Shvetsov, Anders Sildnes, Masoud Tafavvoghi +5
Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) present…
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya +2
Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in m…
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen +1
The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing…
A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images
Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen +5
Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue indicate favourable outcomes in many types of cancer. Manual quantification of immune cells is inaccurate…
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer +2
Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often a…
Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images
Kajsa Møllersen, Maciel Zortea, Thomas R. Schopf +2
Melanoma is the deadliest form of skin cancer. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classif…
Bidirectional deep-readout echo state networks
Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse +1
We propose a deep architecture for the classification of multivariate time series. By means of a recurrent and untrained reservoir we generate a vectorial representation that embed…
On Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering
Kajsa Møllersen, Subhra S. Dhar, Fred Godtliebsen
Hybrid clustering combines partitional and hierarchical clustering for computational effectiveness and versatility in cluster shape. In such clustering, a dissimilarity measure pla…
Computer-Aided Decision Support for Melanoma Detection Applied on Melanocytic and Nonmelanocytic Skin Lesions: A Comparison of Two Systems Based on Automatic Analysis of Dermoscopic Images
Kajsa Møllersen, Herbert Kirchesch, Maciel Zortea +3
Commercially available clinical decision support systems (CDSSs) for skin cancer have been designed for the detection of melanoma only. Correct use of the systems requires expert k…