108 citations
7 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…