9.8k citations · 10.8k across the 28 of their papers we have counts for
8 papers · 1 filter
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
David Zimmerer, Simon A. A. Kohl, Jens Petersen +2
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detec…
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +4
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-t…
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…
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Fabian Isensee, Jens Petersen, Andre Klein +8
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation…
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…
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
Raghavendra Selvan, Jens Petersen, Jesper H Pedersen +1
In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for th…
Extraction of Airways using Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +3
We present extraction of tree structures, such as airways, from image data as a graph refinement task. To this end, we propose a graph auto-encoder model that uses an encoder based…
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
Raghavendra Selvan, Max Welling, Jesper H. Pedersen +2
We present tree extraction in 3D images as a graph refinement task, of obtaining a subgraph from an over-complete input graph. To this end, we formulate an approximate Bayesian inf…