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High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection
Variational Auto-Encoders have often been used for unsupervised pretrain...
11/27/2019 ∙ by David Zimmerer, et al. ∙
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Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans
Supervised feature learning using convolutional neural networks (CNNs) c...
06/19/2018 ∙ by Silas Nyboe Ørting, et al. ∙
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Graph Refinement based Tree Extraction using Mean-Field Networks and Graph Neural Networks
Graph refinement, or the task of obtaining subgraphs of interest from ov...
11/21/2018 ∙ by Raghavendra Selvan, et al. ∙
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Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
In this work, we adapt a method based on multiple hypothesis tracking (M...
06/23/2018 ∙ by Raghavendra Selvan, et al. ∙
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Deep Probabilistic Modeling of Glioma Growth
Existing approaches to modeling the dynamics of brain tumor growth, spec...
07/09/2019 ∙ by Jens Petersen, et al. ∙
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Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing applica...
08/07/2017 ∙ by Raghavendra Selvan, et al. ∙
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Extraction of airway trees using multiple hypothesis tracking and template matching
Knowledge of airway tree morphology has important clinical applications ...
11/24/2016 ∙ by Raghavendra Selvan, et al. ∙
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Geometric tree kernels: Classification of COPD from airway tree geometry
Methodological contributions: This paper introduces a family of kernels ...
03/29/2013 ∙ by Aasa Feragen, et al. ∙
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Mean Field Network based Graph Refinement with application to Airway Tree Extraction
We present tree extraction in 3D images as a graph refinement task, of o...
04/10/2018 ∙ by Raghavendra Selvan, et al. ∙
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Extraction of Airways using Graph Neural Networks
We present extraction of tree structures, such as airways, from image da...
04/12/2018 ∙ by Raghavendra Selvan, et al. ∙
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nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015. With its straight-forward and successfu...
09/27/2018 ∙ by Fabian Isensee, et al. ∙
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Learning to quantify emphysema extent: What labels do we need?
Accurate assessment of pulmonary emphysema is crucial to assess disease ...
10/17/2018 ∙ by Silas Nyboe Ørting, et al. ∙
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Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the ...
12/14/2018 ∙ by David Zimmerer, et al. ∙
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Segmentation of Roots in Soil with U-Net
Plant root research can provide a way to attain stress-tolerant crops th...
02/28/2019 ∙ by Abraham George Smith, et al. ∙
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A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Suppose one is faced with the challenge of tissue segmentation in MR ima...
03/11/2019 ∙ by Wouter M. Kouw, et al. ∙
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nnU-Net: Breaking the Spell on Successful Medical Image Segmentation
Fueled by the diversity of datasets, semantic segmentation is a popular ...
04/17/2019 ∙ by Fabian Isensee, et al. ∙
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Unsupervised Anomaly Localization using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially ove...
07/04/2019 ∙ by David Zimmerer, et al. ∙
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A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
Through training on unlabeled data, anomaly detection has the potential ...
11/28/2019 ∙ by David Zimmerer, et al. ∙
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