33 citations · 55 across the 7 of their papers we have counts for
7 papers
Assessing airborne laser scanning and aerial photogrammetry for deep learning-based stand delineation
Håkon Næss Sandum, Hans Ole Ørka, Oliver Tomic +1
Accurate forest stand delineation is essential for forest inventory and management but remains a largely manual and subjective process. A recent study has shown that deep learning…
Semantic segmentation of forest stands using deep learning
Håkon Næss Sandum, Hans Ole Ørka, Oliver Tomic +2
Forest stands are the fundamental units in forest management inventories, silviculture, and financial analysis within operational forestry. Over the past two decades, a common meth…
Towards Understanding the Survival of Patients with High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms: An Investigation of Ensemble Feature Selection in the Prediction of Overall Survival
Anna Jenul, Henning Langen Stokmo, Stefan Schrunner +3
Determining the most informative features for predicting the overall survival of patients diagnosed with high-grade gastroenteropancreatic neuroendocrine neoplasms is crucial to im…
Ranking Feature-Block Importance in Artificial Multiblock Neural Networks
Anna Jenul, Stefan Schrunner, Bao Ngoc Huynh +4
In artificial neural networks, understanding the contributions of input features on the prediction fosters model explainability and delivers relevant information about the dataset.…
A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)
Anna Jenul, Stefan Schrunner, Jürgen Pilz +1
Feature selection represents a measure to reduce the complexity of high-dimensional datasets and gain insights into the systematic variation in the data. This aspect is of specific…
RENT -- Repeated Elastic Net Technique for Feature Selection
Anna Jenul, Stefan Schrunner, Kristian Hovde Liland +3
Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing pr…
Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers
Yngve Mardal Moe, Aurora Rosvoll Groendahl, Martine Mulstad +5
An automatic segmentation algorithm for delineation of the gross tumour volume and pathologic lymph nodes of head and neck cancers in PET/CT images is described. The proposed algor…