b'Philips'

research

∙ 04/30/2019

CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation

Infarcted brain tissue resulting from acute stroke readily shows up as h...

Jonathan Rubin, et al. ∙

research

∙ 04/15/2020

An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction: Application to the 2019 fastMRI Challenge

Adaptive intelligence aims at empowering machine learning techniques wit...

Nicola Pezzotti, et al. ∙

research

∙ 03/17/2020

3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images

Automatic segmentation of anatomical structures with convolutional neura...

Cheryl Sital, et al. ∙

research

∙ 07/22/2019

Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks

Accurately forecasting urban development and its environmental and clima...

Adrian Albert, et al. ∙

research

∙ 02/08/2022

Untrimmed Action Anticipation

Egocentric action anticipation consists in predicting a future action th...

Ivan Rodin, et al. ∙

research

∙ 07/11/2017

Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines

We provide preliminary details and formulation of an optimization strate...

Sergio Consoli, et al. ∙

research

∙ 05/25/2017

Generating Time-Based Label Refinements to Discover More Precise Process Models

Process mining is a research field focused on the analysis of event data...

Niek Tax, et al. ∙

research

∙ 05/25/2017

Mining Process Model Descriptions of Daily Life through Event Abstraction

Process mining techniques focus on extracting insight in processes from ...

Niek Tax, et al. ∙

research

∙ 07/16/2017

An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit

Our work focuses on the problem of predicting the transfer of pediatric ...

Jonathan Rubin, et al. ∙

research

∙ 12/18/2015

Can Pretrained Neural Networks Detect Anatomy?

Convolutional neural networks demonstrated outstanding empirical results...

Vlado Menkovski, et al. ∙

research

∙ 07/14/2017

Recognizing Abnormal Heart Sounds Using Deep Learning

The work presented here applies deep learning to the task of automated c...

Jonathan Rubin, et al. ∙

research

∙ 07/07/2017

Design and Processing of Invertible Orientation Scores of 3D Images for Enhancement of Complex Vasculature

The enhancement and detection of elongated structures in noisy image dat...

M. H. J. Janssen, et al. ∙

research

∙ 10/10/2016

Heuristic Approaches for Generating Local Process Models through Log Projections

Local Process Model (LPM) discovery is focused on the mining of a set of...

Niek Tax, et al. ∙

research

∙ 09/12/2016

On Generation of Time-based Label Refinements

Process mining is a research field focused on the analysis of event data...

Niek Tax, et al. ∙

research

∙ 03/22/2017

Deeply-Supervised CNN for Prostate Segmentation

Prostate segmentation from Magnetic Resonance (MR) images plays an impor...

Qikui Zhu, et al. ∙

research

∙ 04/25/2014

Multiscale Event Detection in Social Media

Event detection has been one of the most important research topics in so...

Xiaowen Dong, et al. ∙

research

∙ 12/06/2016

Condensed Memory Networks for Clinical Diagnostic Inferencing

Diagnosis of a clinical condition is a challenging task, which often req...

Aaditya Prakash, et al. ∙

research

∙ 10/10/2016

Neural Paraphrase Generation with Stacked Residual LSTM Networks

In this paper, we propose a novel neural approach for paraphrase generat...

Aaditya Prakash, et al. ∙

research

∙ 06/20/2016

A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language

Semantic roles play an important role in extracting knowledge from text....

Vivek Datla, et al. ∙

research

∙ 11/15/2017

Personalized Driver Stress Detection with Multi-task Neural Networks using Physiological Signals

Stress can be seen as a physiological response to everyday emotional, me...

Aaqib Saeed, et al. ∙

research

∙ 08/19/2017

Designing virus-resistant, high-performance networks: a game-formation approach

Designing an optimal network topology while balancing multiple, possibly...

Stojan Trajanovski, et al. ∙

research

∙ 02/15/2018

DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference

We present a novel deep learning architecture to address the natural lan...

Reza Ghaeini, et al. ∙

research

∙ 04/20/2018

Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks

The MIMIC-CXR dataset is (to date) the largest publicly released chest x...

Jonathan Rubin, et al. ∙

research

∙ 06/14/2018

Maximum weight spectrum codes with reduced length

A q-ary linear code of dimension k is called a maximum weight spectrum (...

Gérard D. Cohen, et al. ∙

research

∙ 07/02/2018

Minimum Labelling bi-Connectivity

A labelled, undirected graph is a graph whose edges have assigned labels...

Josè Andrès Moreno Pèrez, et al. ∙

research

∙ 06/25/2018

Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable

In this paper we study the prediction of heart rate from acceleration us...

Ryan McConville, et al. ∙

research

∙ 08/27/2018

Learning behavioral context recognition with multi-stream temporal convolutional networks

Smart devices of everyday use (such as smartphones and wearables) are in...

Aaqib Saeed, et al. ∙

research

∙ 10/21/2018

Patient Subtyping with Disease Progression and Irregular Observation Trajectories

Patient subtyping based on temporal observations can lead to significant...

Nikhil Galagali, et al. ∙

research

∙ 10/24/2018

Clinical Concept Extraction with Contextual Word Embedding

Automatic extraction of clinical concepts is an essential step for turni...

Henghui Zhu, et al. ∙

research

∙ 11/02/2018

Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss

We present a fully convolutional neural network for segmenting ischemic ...

S. Mazdak Abulnaga, et al. ∙

research

∙ 01/02/2019

Evolutionary Construction of Convolutional Neural Networks

Neuro-Evolution is a field of study that has recently gained significant...

Marijn van Knippenberg, et al. ∙

research

∙ 06/25/2019

Learning a sparse database for patch-based medical image segmentation

We introduce a functional for the learning of an optimal database for pa...

Moti Freiman, et al. ∙

research

∙ 07/27/2019

Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI

The quantification of myocardial perfusion MRI has the potential to prov...

Cian M. Scannell, et al. ∙

research

∙ 08/15/2019

Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging

Limitations on bandwidth and power consumption impose strict bounds on d...

Iris A. M. Huijben, et al. ∙

research

∙ 09/12/2019

Perceptual Image Anomaly Detection

We present a novel method for image anomaly detection, where algorithms ...

Nina Tuluptceva, et al. ∙

research

∙ 01/16/2020

Continual Learning for Domain Adaptation in Chest X-ray Classification

Over the last years, Deep Learning has been successfully applied to a br...

Matthias Lenga, et al. ∙

research

∙ 02/12/2020

Service Selection using Predictive Models and Monte-Carlo Tree Search

This article proposes a method for automated service selection to improv...

Cliff Laschet, et al. ∙

research

∙ 02/17/2020

4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model

We propose a hybrid controllable image generation method to synthesize a...

Samaneh Abbasi-Sureshjani, et al. ∙

research

∙ 03/04/2021

Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution

We went below the MRI acceleration factors (a.k.a., k-space undersamplin...

Aleksandr Belov, et al. ∙

research

∙ 08/06/2021

Medical image segmentation with imperfect 3D bounding boxes

The development of high quality medical image segmentation algorithms de...

Ekaterina Redekop, et al. ∙

research

∙ 09/06/2021

Active Learning for Automated Visual Inspection of Manufactured Products

Quality control is a key activity performed by manufacturing enterprises...

Elena Trajkova, et al. ∙

research

∙ 09/15/2021

Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series

Time series models with recurrent neural networks (RNNs) can have high a...

Asif Rahman, et al. ∙

research

∙ 09/29/2021

Convolution-Free Waveform Transformers for Multi-Lead ECG Classification

We present our entry to the 2021 PhysioNet/CinC challenge - a waveform t...

Annamalai Natarajan, et al. ∙

research

∙ 10/15/2021

Streaming Machine Learning and Online Active Learning for Automated Visual Inspection

Quality control is a key activity performed by manufacturing companies t...

Jože M. Rožanec, et al. ∙

research

∙ 10/26/2021

Min-similarity association rules for identifying past comorbidities of recurrent ED and inpatient patients

In the hospital setting, a small percentage of recurrent frequent patien...

Luoluo Liu, et al. ∙

research

∙ 11/01/2021

Simulating Realistic MRI variations to Improve Deep Learning model and visual explanations using GradCAM

In the medical field, landmark detection in MRI plays an important role ...

Muhammad Ilyas Patel, et al. ∙

research

∙ 11/03/2021

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

Accurate, real-time segmentation of vessel structures in ultrasound imag...

Baichuan Jiang, et al. ∙

research

∙ 11/05/2021

Predicting Antimicrobial Resistance in the Intensive Care Unit

Antimicrobial resistance (AMR) is a risk for patients and a burden for t...

Taiyao Wang, et al. ∙

research

∙ 11/25/2021

Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation

Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI)...

Hadas Ben-Atya, et al. ∙

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