Ryutaro Tanno

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Microsoft

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UCL

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∙ 09/25/2021

A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging

Machine learning models commonly exhibit unexpected failures post-deploy...

Thomas Henn, et al. ∙

∙ 09/22/2021

Learning to Downsample for Segmentation of Ultra-High Resolution Images

Segmentation of ultra-high resolution images with deep learning is chall...

Chen Jin, et al. ∙

∙ 07/31/2020

Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

Recent years have seen increasing use of supervised learning methods for...

Le Zhang, et al. ∙

∙ 07/29/2020

Foveation for Segmentation of Ultra-High Resolution Images

Segmentation of ultra-high resolution images is challenging because of t...

Chen Jin, et al. ∙

∙ 03/16/2020

Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients

1.5T or 3T scanners are the current standard for clinical MRI, but low-f...

Matteo Figini, et al. ∙

∙ 09/16/2019

Reproducibility of an airway tapering measurement in CT with application to bronchiectasis

Purpose: This paper proposes a pipeline to acquire a scalar tapering mea...

Kin Quan, et al. ∙

∙ 09/15/2019

Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator

MR images scanned at low magnetic field (<1T) have lower resolution in t...

Hongxiang Lin, et al. ∙

∙ 09/14/2019

Tapering Analysis of Airways with Bronchiectasis

Bronchiectasis is the permanent dilation of airways. Patients with the d...

Kin Quan, et al. ∙

∙ 09/04/2019

Let's agree to disagree: learning highly debatable multirater labelling

Classification and differentiation of small pathological objects may gre...

Carole H Sudre, et al. ∙

∙ 08/26/2019

Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels

The performance of multi-task learning in Convolutional Neural Networks ...

Felix J. S. Bragman, et al. ∙

∙ 07/31/2019

Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement

Deep learning (DL) has shown great potential in medical image enhancemen...

Ryutaro Tanno, et al. ∙

∙ 07/26/2019

Multi-Stage Prediction Networks for Data Harmonization

In this paper, we introduce multi-task learning (MTL) to data harmonizat...

Stefano B. Blumberg, et al. ∙

∙ 06/28/2019

Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection

Numerous lung diseases, such as idiopathic pulmonary fibrosis (IPF), exh...

Kin Quan, et al. ∙

∙ 02/10/2019

Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

The predictive performance of supervised learning algorithms depends on ...

Ryutaro Tanno, et al. ∙

∙ 08/16/2018

Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

In this paper we address the memory demands that come with the processin...

Stefano B. Blumberg, et al. ∙

∙ 07/17/2018

Adaptive Neural Trees

Deep neural networks and decision trees operate on largely separate para...

Ryutaro Tanno, et al. ∙

∙ 06/18/2018

Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

Multi-task neural network architectures provide a mechanism that jointly...

Felix J. S. Bragman, et al. ∙

∙ 06/07/2018

Semi-Supervised Learning via Compact Latent Space Clustering

We present a novel cost function for semi-supervised learning of neural ...

Konstantinos Kamnitsas, et al. ∙