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David Vazquez
Researcher at James Cook University
Publications - 21
Citations - 407
David Vazquez is an academic researcher from James Cook University. The author has contributed to research in topics: Segmentation & Deep learning. The author has an hindex of 8, co-authored 21 publications receiving 171 citations.
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Proceedings ArticleDOI
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Issam H. Laradji,Pau Rodríguez,Oscar Mañas,Keegan Lensink,Marco Law,Lironne Kurzman,William Parker,David Vazquez,Derek Nowrouzezahrai +8 more
TL;DR: Laradji et al. as discussed by the authors proposed a consistency-based loss function that encourages the output predictions to be consistent with spatial transformations of the input images to detect COVID-19 in chest CT images.
Journal ArticleDOI
A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis.
Alzayat Saleh,Issam H. Laradji,Dmitry A. Konovalov,Michael Bradley,David Vazquez,Marcus Sheaves +5 more
TL;DR: DeepFish as discussed by the authors is a large-scale dataset for underwater computer vision tasks, which consists of approximately 40,000 images collected underwater from 20 habitats in the marine-environments of tropical Australia.
Proceedings Article
Where are the Masks: Instance Segmentation with Image-level Supervision
TL;DR: A novel framework that can effectively train with image-level labels, which are significantly cheaper to acquire, and achieves new state-of-the-art results for this problem setup is proposed.
Journal ArticleDOI
A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis
Alzayat Saleh,Issam H. Laradji,Dmitry A. Konovalov,Michael Bradley,David Vazquez,Marcus Sheaves +5 more
TL;DR: This work presents DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks, and collects point-level and segmentation labels to have a more comprehensive fish analysis benchmark.
Posted Content
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Issam H. Laradji,Pau Rodríguez,Oscar Mañas,Keegan Lensink,Marco Law,Lironne Kurzman,William Parker,David Vazquez,Derek Nowrouzezahrai +8 more
TL;DR: A consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images, and yields significant improvement over conventional point-level loss functions and almost matches the performance of models trained with full supervision with much less human effort.