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Article
Open AccessAUCResha**: improved sensitivity at high-specificity
The evaluation of deep-learning (DL) systems typically relies on the Area under the Receiver-Operating-Curve (AU-ROC) as a performance metric. However, AU-ROC, in its holistic form, does not sufficiently consi...
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Article
Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort
To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs.
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Article
Open AccessAutomated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks
With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural net...
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Chapter and Conference Paper
Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures and reducing the risks for vanishing gradients. The...
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Chapter and Conference Paper
Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce readin...
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Chapter and Conference Paper
3D Organ Shape Reconstruction from Topogram Images
Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up. However, addressing this p...
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Chapter and Conference Paper
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater variability and inherent ambigu...
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Chapter and Conference Paper
Nonlinear Adaptively Learned Optimization for Object Localization in 3D Medical Images
Precise localization of anatomical structures in 3D medical images can support several tasks such as image registration, organ segmentation, lesion quantification and abnormality detection. This work proposes ...
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Chapter and Conference Paper
Abstract: Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data
An essential prerequisite for comprehensive medical image analysis is the robust and fast detection of anatomical structures in the human body. To this point, machine learning techniques are most often applied...
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Chapter and Conference Paper
Robust Multi-scale Anatomical Landmark Detection in Incomplete 3D-CT Data
Robust and fast detection of anatomical structures is an essential prerequisite for the next-generation automated medical support tools. While machine learning techniques are most often applied to address this...
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Chapter and Conference Paper
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is...
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Chapter
Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local Versus Global Image Context
of every ten adults in USA (over 20 million). Computed tomography (CT) is a widely used imaging modality for kidney disease diagnosis and quantification. However, automatic pathological kidney is ...
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Chapter
Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning
Recently, success in computer vision with the capability to learn powerful image features from a large training set. However, most of the published work has been confined to solving 2D problems, with...
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Chapter and Conference Paper
Enhancing Place Recognition Using Joint Intensity - Depth Analysis and Synthetic Data
Visual place recognition is an important tool for robots to localize themselves in their surroundings by matching previously seen images. Recent methods based on Convolutional Neural Networks (CNN) are capable...
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Chapter and Conference Paper
An Artificial Agent for Anatomical Landmark Detection in Medical Images
Fast and robust detection of anatomical structures or pathologies represents a fundamental task in medical image analysis. Most of the current solutions are however suboptimal and unconstrained by learning an ...
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Chapter and Conference Paper
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data
Recently, deep learning has demonstrated great success in computer vision with the capability to learn powerful image features from a large training set. However, most of the published work has been confined t...
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Chapter and Conference Paper
Estimation of Regional Electrical Properties of the Heart from 12-Lead ECG and Images
Computational models of cardiac electrophysiology are being investigated for improved patient selection and planning of therapies like cardiac resynchronization therapy (CRT). However, their clinical applicabi...
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Chapter and Conference Paper
Marginal Space Deep Learning: Efficient Architecture for Detection in Volumetric Image Data
Current state-of-the-art techniques for fast and robust parsing of volumetric medical image data exploit large annotated image databases and are typically based on machine learning methods. Two main challenges...
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Chapter and Conference Paper
Robust Live Tracking of Mitral Valve Annulus for Minimally-Invasive Intervention Guidance
Mitral valve (MV) regurgitation is an important cardiac disorder that affects 2-3% of the Western population. While valve repair is commonly performed under open-heart surgery, an increasing number of transcat...
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Chapter and Conference Paper
Vito – A Generic Agent for Multi-physics Model Personalization: Application to Heart Modeling
Precise estimation of computational physiological model parameters from patient data is one of the main hurdles towards their clinical applicability. Designing robust estimation algorithms is often a tedious a...
