output
20132025
most citedA Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

164 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2018Show all

5 papers · 1 filter

cs.CV2018

Learning to quantify emphysema extent: What labels do we need?

Silas Nyboe Ørting, Jens Petersen, Laura H. Thomsen +2

Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression and to predict lung cancer risk. However, visual assess…

cs.CV2018

A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning

Mikael Agn, Per Munck af Rosenschöld, Oula Puonti +7

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method comb…

cs.CV2018★ 1 cited

Deep Learning from Label Proportions for Emphysema Quantification

Gerda Bortsova, Florian Dubost, Silas Ørting +5

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts…

stat.ML2018★ 1 cited

Probabilistic PARAFAC2

Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich +3

The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of difference…

cs.CV2018

Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans

Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina +3

Supervised feature learning using convolutional neural networks (CNNs) can provide concise and disease relevant representations of medical images. However, training CNNs requires a…