2.7k citations · 3.9k across the 29 of their papers we have counts for
33 papers
CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detection
Lucas He, Hanyuan Zhang, Krinos Li +7
Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), but their small size, sparsity,…
A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces
Lucas He, Krinos Li, Hanyuan Zhang +7
Cerebral small vessel disease (CSVD) markers, specifically enlarged perivascular spaces (EPVS) and lacunae, present a unique challenge in medical image analysis due to their radiol…
Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals
Pascaline André, Charles Heitz, Evangelia Christodoulou +10
Performance uncertainty quantification is essential for reliable validation and eventual clinical translation of medical imaging artificial intelligence (AI). Confidence intervals…
Medical Imaging AI Competitions Lack Fairness
Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda +34
Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. How…
False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims
Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23
Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
Evangelia Christodoulou, Annika Reinke, Rola Houhou +19
Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost +46
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Evaluation of automated airway morphological quantification for assessing fibrosing lung disease
Ashkan Pakzad, Wing Keung Cheung, Kin Quan +21
Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF). Volumetric computed tomography (CT) imaging captures the lo…
Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets
Martyn Fyles, Karina-Doris Vihta, Carole H Sudre +13
Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-pos…
A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +2
Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visua…
Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks
Mariana Da Silva, Carole H. Sudre, Kara Garcia +3
Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hype…
Estimating MRI Image Quality via Image Reconstruction Uncertainty
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +1
Quality control (QC) in medical image analysis is time-consuming and laborious, leading to increased interest in automated methods. However, what is deemed suitable quality for alg…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans
Cher Bass, Mariana da Silva, Carole Sudre +7
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree…
Biomechanical modelling of brain atrophy through deep learning
Mariana da Silva, Kara Garcia, Carole H. Sudre +3
We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations. Using prescribed maps of local atrophy and growth as in…
Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
Benjamin Murray, Eric Kerfoot, Mark S. Graham +14
The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and…
Test-time Unsupervised Domain Adaptation
Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre +3
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain…
Hierarchical brain parcellation with uncertainty
Mark S. Graham, Carole H. Sudre, Thomas Varsavsky +4
Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods ten…
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
Reuben Dorent, Thomas Booth, Wenqi Li +5
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Cher Bass, Mariana da Silva, Carole Sudre +3
Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within…