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AI for social good: unlocking the opportunity for positive impact

Nature communications, 2020-05, Vol.11 (1), p.2468-2468, Article 2468

2020

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A clinically applicable approach to continuous prediction of future acute kidney injury

Nature (London), 2019-08, Vol.572 (7767), p.116-119

2019

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On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo

Statistical applications in genetics and molecular biology, 2013-03, Vol.12 (1), p.87-107

2013

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Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; peer review: 2 approved]

F1000 research, 2016, Vol.5, p.1573

2016

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The Importance of Skip Connections in Biomedical Image Segmentation

Deep Learning and Data Labeling for Medical Applications, p.179-187

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Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks

Deep Learning and Data Labeling for Medical Applications, p.170-178

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Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; peer review: 2 approved]

F1000 research, 2016-01, Vol.5, p.1573-1573

2016

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A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography

Deep Learning and Data Labeling for Medical Applications, p.197-205

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Fully Convolutional Network for Liver Segmentation and Lesions Detection

Deep Learning and Data Labeling for Medical Applications, p.77-85

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Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings

1, 2016

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Understanding the Mechanisms of Deep Transfer Learning for Medical Images

Deep Learning and Data Labeling for Medical Applications, p.188-196

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Fast Predictive Image Registration

Deep Learning and Data Labeling for Medical Applications, p.48-57

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Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data

Deep Learning and Data Labeling for Medical Applications, p.142-151

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Adaptive sequential Monte Carlo by means of mixture of experts

Statistics and computing, 2014-05, Vol.24 (3), p.317-337

2014

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Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting

Deep Learning and Data Labeling for Medical Applications, p.111-120

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Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features

Deep Learning and Data Labeling for Medical Applications, p.39-47

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Adaptive methods for sequential importance sampling with application to state space models

2008 16th European Signal Processing Conference, 2008-12, Vol.18 (4), p.1-5

2008

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Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Deep Learning and Data Labeling for Medical Applications, 2016, p.30-38

2016

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Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networks

Deep Learning and Data Labeling for Medical Applications, p.58-67

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Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans

Deep Learning and Data Labeling for Medical Applications, p.238-248

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Adaptive Markov chain Monte Carlo forward projection for statistical analysis in epidemic modelling of human papillomavirus

Statistics in medicine, 2013-05, Vol.32 (11), p.1917-1953

2013

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Medical Image Description Using Multi-task-loss CNN

Deep Learning and Data Labeling for Medical Applications, p.121-129

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Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis

Deep Learning and Data Labeling for Medical Applications, p.86-94

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AI for social good: unlocking the opportunity for positive impact

Clinically applicable deep learning for diagnosis and referral in retinal disease

A clinically applicable approach to continuous prediction of future acute kidney injury

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo

Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; peer review: 2 approved]

The Importance of Skip Connections in Biomedical Image Segmentation

Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks

Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; peer review: 2 approved]

A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography

Fully Convolutional Network for Liver Segmentation and Lesions Detection

Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings

Understanding the Mechanisms of Deep Transfer Learning for Medical Images

Fast Predictive Image Registration

Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data

Adaptive sequential Monte Carlo by means of mixture of experts

Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting

Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features

Adaptive methods for sequential importance sampling with application to state space models

Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networks

Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans

Adaptive Markov chain Monte Carlo forward projection for statistical analysis in epidemic modelling of human papillomavirus

Medical Image Description Using Multi-task-loss CNN

Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis

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