74 citations · 349 across the 38 of their papers we have counts for
14 papers · 2 filters
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
Qi Dou, Cheng Ouyang, Cheng Chen +4
Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same an…
Towards continual learning in medical imaging
Chaitanya Baweja, Ben Glocker, Konstantinos Kamnitsas
This work investigates continual learning of two segmentation tasks in brain MRI with neural networks. To explore in this context the capabilities of current methods for countering…
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…
Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza +4
Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstra…
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…
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Xiaoran Chen, Nick Pawlowski, Martin Rajchl +2
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributio…