766 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of TorontoCA15 papers
- Sunnybrook Health Science CentreCA11 papers
- Sunnybrook HospitalCA5 papers
- Imperial College LondonGB3 papers
- InsermFR3 papers
- King's College LondonGB3 papers
- KU LeuvenBE3 papers
- Radboud University Medical CenterNL3 papers
- Radboud University NijmegenNL3 papers
- Universität HamburgDE3 papers
- University College LondonGB3 papers
- University Hospital of BernCH3 papers
18 papers
A generalizable 3D framework and model for self-supervised learning in medical imaging
Tony Xu, Sepehr Hosseini, Chris Anderson +4
Current self-supervised learning methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and sc…
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
Current Methods for Hyperpolarized [1-13C]pyruvate MRI Human Studies
Peder EZ Larson, Jenna ML Bernard, James A Bankson +16
MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney dise…
Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach
Mostafa Sharifzadeh, Sobhan Goudarzi, An Tang +2
One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which…
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…
Deep Learning-Based MR Image Re-parameterization
Abhijeet Narang, Abhigyan Raj, Mihaela Pop +1
Magnetic resonance (MR) image re-parameterization refers to the process of generating via simulations of an MR image with a new set of MRI scanning parameters. Different parameter…
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…
Measuring hand use in the home after cervical spinal cord injury using egocentric video
Andrea Bandini, Mehdy Dousty, Sander L. Hitzig +3
Background: Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its a…
BI-RADS BERT & Using Section Segmentation to Understand Radiology Reports
Grey Kuling, Belinda Curpen, Anne L. Martel
Radiology reports are one of the main forms of communication between radiologists and other clinicians and contain important information for patient care. In order to use this info…
Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study
Matthew Ng, Fumin Guo, Labonny Biswas +4
Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world…
Learning to segment images with classification labels
Ozan Ciga, Anne L. Martel
Two of the most common tasks in medical imaging are classification and segmentation. Either task requires labeled data annotated by experts, which is scarce and expensive to collec…
Deep neural network models for computational histopathology: A survey
Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes. R…
Multi-Group Multicast Beamforming: Optimal Structure and Efficient Algorithms
Min Dong, Qiqi Wang
This paper considers the multi-group multicast beamforming optimization problem, for which the optimal solution has been unknown due to the non-convex and NP-hard nature of the pro…
Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model
Jianan Chen, Laurent Milot, Helen M. C. Cheung +1
Quantitative medical image computing (radiomics) has been widely applied to build prediction models from medical images. However, overfitting is a significant issue in conventional…
Prostate Cancer Detection using Deep Convolutional Neural Networks
Sunghwan Yoo, Isha Gujrathi, Masoom A. Haider +1
Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tool…
Shoulder Physiotherapy Exercise Recognition: Machine Learning the Inertial Signals from a Smartwatch
David Burns, Nathan Leung, Michael Hardisty +3
Objective: Participation in a physical therapy program is considered one of the greatest predictors of successful conservative management of common shoulder disorders. However, adh…
Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering
Lucia Ballerini, Ruggiero Lovreglio, Maria del C. Valdes-Hernandez +4
Perivascular Spaces (PVS) are a recently recognised feature of Small Vessel Disease (SVD), also indicating neuroinflammation, and are an important part of the brain's circulation a…
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields
Edward Li, Farzad Khalvati, Mohammad Javad Shafiee +2
Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However image quality may suffer by long a…