309 citations · 310 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
Francesco Ciompi, Kaman Chung, Sarah J. van Riel +10
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decid…