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Article
Open AccessSmart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation
The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiog...
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Article
Open AccessComparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
The increased availability of labeled X-ray image archives (e.g. ChestX-ray14 dataset) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and...
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Chapter and Conference Paper
Abstract: Does Bone Suppression and Lung Detection Improve Chest Disease Classification?
Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, researchers started develo** methods for automatic pathology classification. In ...
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Chapter and Conference Paper
How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning
The vast majority of 3D medical images lacks detailed image-based expert annotations. The ongoing advances of deep convolutional neural networks clearly demonstrate the benefit of supervised learning to succes...
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Chapter and Conference Paper
Learning a Sparse Database for Patch-Based Medical Image Segmentation
We introduce a functional for the learning of an optimal database for patch-based image segmentation with application to coronary lumen segmentation from coronary computed tomography angiography (CCTA) data. T...
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Chapter and Conference Paper
Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations
We propose a parametric lumped model (LM) for fast patient-specific computational fluid dynamic simulations of blood flow in elongated vessel networks to alleviate the computational burden of 3D finite element...
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Chapter and Conference Paper
From Image to Personalized Cardiac Simulation: Encoding Anatomical Structures into a Model-Based Segmentation Framework
Whole organ scale patient specific biophysical simulations contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmia. However, many individual steps are required t...
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Article
User-Centric Learning and Evaluation of Interactive Segmentation Systems
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive s...
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Article
Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis
Without non-linear basis functions many problems can not be solved by linear algorithms. This article proposes a method to automatically construct such basis functions with slow feature analysis (SFA). Non-linear...
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Chapter and Conference Paper
Regularized Sparse Kernel Slow Feature Analysis
This paper develops a kernelized slow feature analysis (SFA) algorithm. SFA is an unsupervised learning method to extract features which encode latent variables from time series. Generative relationships are usua...
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Chapter and Conference Paper
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the...
