1 citations · 1 across the 6 of their papers we have counts for
6 papers
The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound
Blake VanBerlo, Alexander Wong, Jesse Hoey +1
Data augmentation is a central component of joint embedding self-supervised learning (SSL). Approaches that work for natural images may not always be effective in medical imaging t…
Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound
Blake VanBerlo, Alexander Wong, Jesse Hoey +1
Self-supervised learning (SSL) is one strategy for addressing the paucity of labelled data in medical imaging by learning representations from unlabelled images. Contrastive and no…
Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks
Blake VanBerlo, Brian Li, Jesse Hoey +1
In this study, we investigated whether self-supervised pretraining could produce a neural network feature extractor applicable to multiple classification tasks in B-mode lung ultra…
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images
Blake VanBerlo, Jesse Hoey, Alexander Wong
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review s…
Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound
Blake VanBerlo, Brian Li, Alexander Wong +2
Self-supervised pretraining has been observed to improve performance in supervised learning tasks in medical imaging. This study investigates the utility of self-supervised pretrai…
Dream to Explore: Adaptive Simulations for Autonomous Systems
Zahra Sheikhbahaee, Dongshu Luo, Blake VanBerlo +3
One's ability to learn a generative model of the world without supervision depends on the extent to which one can construct abstract knowledge representations that generalize acros…