Výsledky vyhledávání - "Nick Pawlowski"

Akademický článek

Exploring Healthy Retinal Aging with Deep Learning

Autor: Martin J. Menten, PhD, Robbie Holland, MSc, Oliver Leingang, PhD, Hrvoje Bogunović, PhD, Ahmed M. Hagag, MD, Rebecca Kaye, MD, Sophie Riedl, MD, Ghislaine L. Traber, MD, Osama N. Hassan, MSc, Nick Pawlowski, PhD, Ben Glocker, PhD, Lars G. Fritsche, PhD, Hendrik P.N. Scholl, MD, Sobha Sivaprasad, MD, Ursula Schmidt-Erfurth, MD, Daniel Rueckert, PhD, Andrew J. Lotery, MD

Publikováno v: Ophthalmology Science, Vol 3, Iss 3, Pp 100294- (2023)

Purpose: To study the individual course of retinal changes caused by healthy aging using deep learning. Design: Retrospective analysis of a large data set of retinal OCT images. Participants: A total of 85 709 adults between the age of 40 and 75 year

Externí odkaz: https://doaj.org/article/e0eb53ffa5f04287a5154c4856cf8732

Zobrazit plný text záznamu

Exploring healthy retinal aging with deep learning

Autor: Martin J. Menten, Robbie Holland, Oliver Leingang, Hrvoje Bogunović, Ahmed M. Hagag, Rebecca Kaye, Sophie Riedl, Ghislaine L. Traber, Osama N. Hassan, Nick Pawlowski, Ben Glocker, Lars G. Fritsche, Hendrik P.N. Scholl, Sobha Sivaprasad, Ursula Schmidt-Erfurth, Daniel Rueckert, Andrew J. Lotery

PurposeTo study the individual course of retinal changes caused by healthy aging using deep learning.DesignRetrospective analysis of a large dataset of retinal optical coherence tomography (OCT) images.ParticipantsEighty-five thousand seven hundred a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::783c8202551081ac200149d12bd95b02
https://eprints.soton.ac.uk/476645/

Zobrazit plný text záznamu

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

Autor: Christos Kaplanis, Nick Pawlowski, Murray Shanahan, Nat Dilokthanakul

Publikováno v: IEEE Transactions on Neural Networks and Learning Systems. 30:3409-3418

One of the main concerns of deep reinforcement learning (DRL) is the data inefficiency problem, which stems both from an inability to fully utilize data acquired and from naive exploration strategies. In order to alleviate these problems, we propose

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d0b7111498b381c5153f67377dec0b7
https://doi.org/10.1109/tnnls.2019.2891792

Zobrazit plný text záznamu

Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions

We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier. We frame this task as an ou

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29f1481559c5e1a317545f81769528df

Zobrazit plný text záznamu

Normative ascent with local gaussians for unsupervised lesion detection

Autor: Ben Glocker, Xiaoran Chen, Nick Pawlowski, Ender Konukoglu

Publikováno v: Medical Image Analysis, 74

Unsupervised abnormality detection is an appealing approach to identify patterns that are not present in training data without specific annotations for such patterns. In the medical imaging field, methods taking this approach have been proposed to de

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0220898de8e7be1dd18f2b55caaf2690
https://pubmed.ncbi.nlm.nih.gov/34487984

Zobrazit plný text záznamu

Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound

Autor: Qingjie Meng, Nick Pawlowski, Bernhard Kainz, Daniel Rueckert

Publikováno v: Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis ISBN: 9783030328740
SUSI/PIPPI@MICCAI
Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis

One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d96948f487311588325474853c8f912
http://arxiv.org/abs/1908.07885

Zobrazit plný text záznamu

Unsupervised Lesion Detection with Locally Gaussian Approximation

Autor: Xiaoran Chen, Nick Pawlowski, Ben Glocker, Ender Konukoglu

Publikováno v: Machine Learning in Medical Imaging ISBN: 9783030326913
MLMI@MICCAI

Generative models have recently been applied to unsupervised lesion detection, where a distribution of normal data, i.e. the normative distribution, is learned and lesions are detected as out-of-distribu-tion regions. However, directly calculating th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5fed44dfb7ae5f1cbc457d0a27be2b4e
https://doi.org/10.1007/978-3-030-32692-0_41

Zobrazit plný text záznamu

Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI

Autor: Daniel Rueckert, Martin Rajchl, Ben Glocker, Ioannis Lavdas, Vanya V. Valindria, Nick Pawlowski, Andrea Rockall, Eric O. Aboagye

Publikováno v: IEEE Winter Conference on Applications of Computer Vision
WACV

Convolutional neural networks have been widely used in medical image segmentation. The amount of training data strongly determines the overall performance. Most approaches are applied for a single imaging modality, e.g., brain MRI. In practice, it is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bc3a7f9820296735099135c77a011d3
http://hdl.handle.net/10044/1/56452

Zobrazit plný text záznamu

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Autor: Matthew C. H. Lee, Bernhard Kainz, Steven McDonagh, Enzo Ferrante, Matthew Sinclair, Martin Rajchl, Daniel Rueckert, Konstantinos Kamnitsas, Wenjia Bai, Ben Glocker, Nick Pawlowski

Publikováno v: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783319752372
BrainLes@MICCAI
MICCAI BrainLes Workshop

Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::422b7a7d80e2f8da0e7540b2b49f1bcf
http://hdl.handle.net/10044/1/54498

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání