no code implementations • 11 Jan 2024 • Tao Tu, Anil Palepu, Mike Schaekermann, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Nenad Tomasev, Shekoofeh Azizi, Karan Singhal, Yong Cheng, Le Hou, Albert Webson, Kavita Kulkarni, S Sara Mahdavi, Christopher Semturs, Juraj Gottweis, Joelle Barral, Katherine Chou, Greg S Corrado, Yossi Matias, Alan Karthikesalingam, Vivek Natarajan
The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors.
no code implementations • 8 Dec 2023 • Talfan Evans, Shreya Pathak, Hamza Merzic, Jonathan Schwarz, Ryutaro Tanno, Olivier J. Henaff
Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow.
no code implementations • 30 Nov 2023 • Ryutaro Tanno, David G. T. Barrett, Andrew Sellergren, Sumedh Ghaisas, Sumanth Dathathri, Abigail See, Johannes Welbl, Karan Singhal, Shekoofeh Azizi, Tao Tu, Mike Schaekermann, Rhys May, Roy Lee, SiWai Man, Zahra Ahmed, Sara Mahdavi, Yossi Matias, Joelle Barral, Ali Eslami, Danielle Belgrave, Vivek Natarajan, Shravya Shetty, Pushmeet Kohli, Po-Sen Huang, Alan Karthikesalingam, Ira Ktena
Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment.
1 code implementation • 18 Oct 2023 • Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare
To address this challenge, we introduce a framework for Multi-Concept Prompt Learning (MCPL), where multiple new "words" are simultaneously learned from a single sentence-image pair.
no code implementations • 26 Jul 2023 • Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Chuck Lau, Ryutaro Tanno, Ira Ktena, Basil Mustafa, Aakanksha Chowdhery, Yun Liu, Simon Kornblith, David Fleet, Philip Mansfield, Sushant Prakash, Renee Wong, Sunny Virmani, Christopher Semturs, S Sara Mahdavi, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Joelle Barral, Dale Webster, Greg S. Corrado, Yossi Matias, Karan Singhal, Pete Florence, Alan Karthikesalingam, Vivek Natarajan
While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.
1 code implementation • 26 Apr 2023 • Hongxiang Lin, Matteo Figini, Felice D'Arco, Godwin Ogbole, Ryutaro Tanno, Stefano B. Blumberg, Lisa Ronan, Biobele J. Brown, David W. Carmichael, Ikeoluwa Lagunju, Judith Helen Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander
Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field.
no code implementations • 18 Apr 2023 • Ira Ktena, Olivia Wiles, Isabela Albuquerque, Sylvestre-Alvise Rebuffi, Ryutaro Tanno, Abhijit Guha Roy, Shekoofeh Azizi, Danielle Belgrave, Pushmeet Kohli, Alan Karthikesalingam, Taylan Cemgil, Sven Gowal
In our work, we show that learning realistic augmentations automatically from data is possible in a label-efficient manner using generative models.
no code implementations • 11 Jul 2022 • Ryutaro Tanno, Melanie F. Pradier, Aditya Nori, Yingzhen Li
Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases.
1 code implementation • 25 Sep 2021 • Thomas Henn, Yasukazu Sakamoto, Clément Jacquet, Shunsuke Yoshizawa, Masamichi Andou, Stephen Tchen, Ryosuke Saga, Hiroyuki Ishihara, Katsuhiko Shimizu, Yingzhen Li, Ryutaro Tanno
We suggest that the quality of the identified failure types can be validated through measuring the intra- and inter-type generalisation after fine-tuning and introduce metrics to compare different subtyping methods.
1 code implementation • ICLR 2022 • Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, Daniel C. Alexander
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.
1 code implementation • 1 Sep 2021 • Melanie Bernhardt, Daniel C. Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem C. Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, Ozan Oktay
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment of model performance.
1 code implementation • NeurIPS 2020 • Le Zhang, Ryutaro Tanno, MouCheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel Alexander
In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large.
3 code implementations • 31 Jul 2020 • Le Zhang, Ryutaro Tanno, Mou-Cheng Xu, Chen Jin, Joseph Jacob, Olga Ciccarelli, Frederik Barkhof, Daniel C. Alexander
Recent years have seen increasing use of supervised learning methods for segmentation tasks.
1 code implementation • 29 Jul 2020 • Chen Jin, Ryutaro Tanno, Mou-Cheng Xu, Thomy Mertzanidou, Daniel C. Alexander
We demonstrate on three publicly available high-resolution image datasets that the foveation module consistently improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off.
no code implementations • 16 Mar 2020 • Matteo Figini, Hongxiang Lin, Godwin Ogbole, Felice D Arco, Stefano B. Blumberg, David W. Carmichael, Ryutaro Tanno, Enrico Kaden, Biobele J. Brown, Ikeoluwa Lagunju, Helen J. Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander
1. 5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robustness to power failures.
1 code implementation • 16 Sep 2019 • Kin Quan, Ryutaro Tanno, Rebecca J. Shipley, Jeremy S. Brown, Joseph Jacob, John R. Hurst, David J. Hawkes
Purpose: This paper proposes a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on CT. We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure.
no code implementations • 15 Sep 2019 • 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
In this paper we propose a probabilistic decimation simulator to improve robustness of model training.
2 code implementations • 14 Sep 2019 • Kin Quan, Rebecca J. Shipley, Ryutaro Tanno, Graeme McPhillips, Vasileios Vavourakis, David Edwards, Joseph Jacob, John R. Hurst, David J. Hawkes
We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis.
no code implementations • 4 Sep 2019 • 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
Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time.
no code implementations • ICCV 2019 • Felix J. S. Bragman, Ryutaro Tanno, Sebastien Ourselin, Daniel C. Alexander, M. Jorge Cardoso
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on the design of feature sharing between tasks within the architecture.
no code implementations • 31 Jul 2019 • Ryutaro Tanno, Daniel Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution.
no code implementations • 26 Jul 2019 • Stefano B. Blumberg, Marco Palombo, Can Son Khoo, Chantal M. W. Tax, Ryutaro Tanno, Daniel C. Alexander
Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison.
1 code implementation • 28 Jun 2019 • Kin Quan, Ryutaro Tanno, Michael Duong, Arjun Nair, Rebecca Shipley, Mark Jones, Christopher Brereton, John Hurst, David Hawkes, Joseph Jacob
We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression.
1 code implementation • CVPR 2019 • Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman
We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.
1 code implementation • 16 Aug 2018 • Stefano B. Blumberg, Ryutaro Tanno, Iasonas Kokkinos, Daniel C. Alexander
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.
1 code implementation • ICLR 2019 • Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya Nori
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.
no code implementations • 18 Jun 2018 • 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
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources.
no code implementations • ICML 2018 • Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.
no code implementations • 1 May 2017 • Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs).