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- research-articleJune 2023
Learning from multiple annotators for medical image segmentation
- Le Zhang,
- Ryutaro Tanno,
- Moucheng Xu,
- Yawen Huang,
- Kevin Bronik,
- Chen Jin,
- Joseph Jacob,
- Yefeng Zheng,
- Ling Shao,
- Olga Ciccarelli,
- Frederik Barkhof,
- Daniel C. Alexander
- A novel deep CNN architecture is proposed for jointly learning the expert consensus label and the annotator’s label. The proposed ...
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AbstractSupervised machine learning methods have been widely developed for segmentation tasks in recent years. However, the quality of labels has high impact on the predictive performance of these algorithms. This issue is particularly acute ...
- research-articleApril 2024
Repairing neural networks by leaving the right past behind
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing SystemsNovember 2022, Article No.: 954, Pages 13132–13145Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases. However, such data points that are responsible for a given failure mode are generally not known a ...
- ArticleSeptember 2021
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging
- Thomas Henn,
- Yasukazu Sakamoto,
- Clément Jacquet,
- Shunsuke Yoshizawa,
- Masamichi Andou,
- Stephen Tchen,
- Ryosuke Saga,
- Hiroyuki Ishihara,
- Katsuhiko Shimizu,
- Yingzhen Li,
- Ryutaro Tanno
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021Sep 2021, Pages 509–518https://doi.org/10.1007/978-3-030-87199-4_48AbstractMachine learning models commonly exhibit unexpected failures post-deployment due to either data shifts or uncommon situations in the training environment. Domain experts typically go through the tedious process of inspecting the failure cases ...
- research-articleDecember 2020
Disentangling human error from the ground truth in segmentation of medical images
- Le Zhang,
- Ryutaro Tanno,
- Mou-Cheng Xu,
- Chen Jin,
- Joseph Jacob,
- Olga Ciccarelli,
- Frederik Barkhof,
- Daniel C. Alexander
NIPS '20: Proceedings of the 34th International Conference on Neural Information Processing SystemsDecember 2020, Article No.: 1321, Pages 15750–15762Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels. This problem is particularly pertinent in the medical image domain, ...
- ArticleOctober 2020
Foveation for Segmentation of Mega-Pixel Histology Images
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020Oct 2020, Pages 561–571https://doi.org/10.1007/978-3-030-59722-1_54AbstractSegmenting histology images is challenging because of the sheer size of the images with millions or even billions of pixels. Typical solutions pre-process each histology image by dividing it into patches of fixed size and/or down-sampling to meet ...
- ArticleOctober 2020
Learning to Segment When Experts Disagree
- Le Zhang,
- Ryutaro Tanno,
- Kevin Bronik,
- Chen Jin,
- Parashkev Nachev,
- Frederik Barkhof,
- Olga Ciccarelli,
- Daniel C. Alexander
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020Oct 2020, Pages 179–190https://doi.org/10.1007/978-3-030-59710-8_18AbstractRecent years have seen an increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depend on the quality of labels, especially in medical image domain, where both the annotation ...
- ArticleOctober 2019
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
- Hongxiang Lin,
- Matteo Figini,
- Ryutaro Tanno,
- Stefano B. Blumberg,
- Enrico Kaden,
- Godwin Ogbole,
- Biobele J. Brown,
- Felice D’Arco,
- David W. Carmichael,
- Ikeoluwa Lagunju,
- Helen J. Cross,
- Delmiro Fernandez-Reyes,
- Daniel C. Alexander
Machine Learning for Medical Image ReconstructionOct 2019, Pages 58–70https://doi.org/10.1007/978-3-030-33843-5_6AbstractMR images scanned at low magnetic field (T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those from high field (typically 1.5T and 3T). We adapt the recent idea of ...
- ArticleOctober 2019
Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection
- Kin Quan,
- Ryutaro Tanno,
- Michael Duong,
- Arjun Nair,
- Rebecca Shipley,
- Mark Jones,
- Christopher Brereton,
- John Hurst,
- David Hawkes,
- Joseph Jacob
Machine Learning in Medical ImagingOct 2019, Pages 345–354https://doi.org/10.1007/978-3-030-32692-0_40AbstractNumerous lung diseases, such as idiopathic pulmonary fibrosis (IPF), exhibit dilation of the airways. Accurate measurement of dilatation enables assessment of the progression of disease. Unfortunately the combination of image noise and airway ...
- ArticleOctober 2019
Let’s Agree to Disagree: Learning Highly Debatable Multirater Labelling
- Carole H. Sudre,
- Beatriz Gomez Anson,
- Silvia Ingala,
- Chris D. Lane,
- Daniel Jimenez,
- Lukas Haider,
- Thomas Varsavsky,
- Ryutaro Tanno,
- Lorna Smith,
- Sébastien Ourselin,
- Rolf H. Jäger,
- M. Jorge Cardoso
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019Oct 2019, Pages 665–673https://doi.org/10.1007/978-3-030-32251-9_73AbstractClassification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class ...
- ArticleOctober 2019
Multi-stage Prediction Networks for Data Harmonization
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019Oct 2019, Pages 411–419https://doi.org/10.1007/978-3-030-32251-9_45AbstractIn this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us to integrate information from multiple acquisitions and improve the ...
- ArticleOctober 2019
Learning Task-Specific and Shared Representations in Medical Imaging
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019Oct 2019, Pages 374–383https://doi.org/10.1007/978-3-030-32251-9_41AbstractThe performance of multi-task learning hinges on the design of feature sharing between tasks; a process which is combinatorial in the network depth and task count. Hand-crafting an architecture based on human intuitions of task relationships is ...
- ArticleSeptember 2018
AutoDVT: Joint Real-Time Classification for Vein Compressibility Analysis in Deep Vein Thrombosis Ultrasound Diagnostics
- Ryutaro Tanno,
- Antonios Makropoulos,
- Salim Arslan,
- Ozan Oktay,
- Sven Mischkewitz,
- Fouad Al-Noor,
- Jonas Oppenheimer,
- Ramin Mandegaran,
- Bernhard Kainz,
- Mattias P. Heinrich
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018Sep 2018, Pages 905–912https://doi.org/10.1007/978-3-030-00934-2_100AbstractWe propose a dual-task convolutional neural network (CNN) to fully automate the real-time diagnosis of deep vein thrombosis (DVT). DVT can be reliably diagnosed through evaluation of vascular compressibility at anatomically defined landmarks in ...
- ArticleSeptember 2018
Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning
- Felix J. S. Bragman,
- Ryutaro Tanno,
- Zach Eaton-Rosen,
- Wenqi Li,
- David J. Hawkes,
- Sebastien Ourselin,
- Daniel C. Alexander,
- Jamie R. McClelland,
- M. Jorge Cardoso
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018Sep 2018, Pages 3–11https://doi.org/10.1007/978-3-030-00937-3_1AbstractMulti-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 as it can jointly regress a synthetic CT (synCT) scan and segment ...
- ArticleSeptember 2018
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018Sep 2018, Pages 118–125https://doi.org/10.1007/978-3-030-00928-1_14AbstractIn 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-efficient backpropagation techniques, to reduce the memory complexity ...
- ArticleSeptember 2017
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
- Ryutaro Tanno,
- Daniel E. Worrall,
- Aurobrata Ghosh,
- Enrico Kaden,
- Stamatios N. Sotiropoulos,
- Antonio Criminisi,
- Daniel C. Alexander
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017Sep 2017, Pages 611–619https://doi.org/10.1007/978-3-319-66182-7_70AbstractIn this work, we investigate the value of uncertainty modelling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) ...
- ArticleOctober 2016
Bayesian Image Quality Transfer
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016Oct 2016, Pages 265–273https://doi.org/10.1007/978-3-319-46723-8_31AbstractImage quality transfer (IQT) aims to enhance clinical images of relatively low quality by learning and propagating high-quality structural information from expensive or rare data sets. However, the original framework gives no indication of ...