4.6k citations · 6.3k across the 33 of their papers we have counts for
11 papers · 1 filter
A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks
Giacomo Tarroni, Ozan Oktay, Matthew Sinclair +7
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath…
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
Jo Schlemper, Ozan Oktay, Michiel Schaap +4
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs…
Recurrent neural networks for aortic image sequence segmentation with sparse annotations
Wenjia Bai, Hideaki Suzuki, Chen Qin +4
Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct applic…
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Carlo Biffi, Ozan Oktay, Giacomo Tarroni +9
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular disea…
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Maximilian Seitzer, Guang Yang, Jo Schlemper +9
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE)…
Real-time Prediction of Segmentation Quality
Robert Robinson, Ozan Oktay, Wenjia Bai +17
Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due…
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary, Loic Le Folgoc, Ghislain Vaillant +11
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to pe…
Attention U-Net: Learning Where to Look for the Pancreas
Ozan Oktay, Jo Schlemper, Loic Le Folgoc +9
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implic…
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection
Jo Schlemper, Ozan Oktay, Liang Chen +5
In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challeng…
Learning-Based Quality Control for Cardiac MR Images
Giacomo Tarroni, Ozan Oktay, Wenjia Bai +9
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned…
TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions
Mattias P. Heinrich, Max Blendowski, Ozan Oktay
Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high quality results for common image analysis tasks including se…