142 citations · 1k across the 43 of their papers we have counts for
10 papers · 1 filter
Learning to Downsample for Segmentation of Ultra-High Resolution Images
Chen Jin, Ryutaro Tanno, Thomy Mertzanidou +2
Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. C…
Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms
Loic Peter, Daniel C. Alexander, Caroline Magnain +1
Landmark correspondences are a widely used type of gold standard in image registration. However, the manual placement of corresponding points is subject to high inter-user variabil…
Learning To Pay Attention To Mistakes
Mou-Cheng Xu, Neil P. Oxtoby, Daniel C. Alexander +1
In convolutional neural network based medical image segmentation, the periphery of foreground regions representing malignant tissues may be disproportionately assigned as belonging…
Foveation for Segmentation of Ultra-High Resolution Images
Chen Jin, Ryutaro Tanno, Moucheng Xu +2
Segmentation of ultra-high resolution images is challenging because of their enormous size, consisting of millions or even billions of pixels. Typical solutions include dividing in…
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Le Zhang, Ryutaro Tanno, Mou-Cheng Xu +5
Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of label…
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
Felix J. S. Bragman, Ryutaro Tanno, Sebastien Ourselin +2
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on the design of feature sharing between tasks within the architecture. The number of possible…
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
Razvan V. Marinescu, Arman Eshaghi, Marco Lorenzi +5
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-te…
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images
Stefano B. Blumberg, Ryutaro Tanno, Iasonas Kokkinos +1
In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-effi…
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Felix J. S. Bragman, Ryutaro Tanno, Zach Eaton-Rosen +6
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning a…
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh +4
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of me…