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Found 7 papers, 1 papers with code
Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound
Guidelines for practitioners were synthesized based on the results, such as the merit of IVPP with task-specific hyperparameters, and the improved performance of contrastive methods for ultrasound compared to non-contrastive counterparts.
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data.
Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks
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 ultrasound analysis.
Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound
This study investigates the utility of self-supervised pretraining prior to conducting supervised fine-tuning for the downstream task of lung sliding classification in M-mode lung ultrasound images.
Dream to Explore: Adaptive Simulations for Autonomous Systems
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 across experiences.
Univariate Long-Term Municipal Water Demand Forecasting
This study describes an investigation into the modelling of citywide water consumption in London, Canada.
Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness
We introduce a machine learning approach to predict chronic homelessness from de-identified client shelter records drawn from a commonly used Canadian homelessness management information system.
BIG-bench Machine Learning
Interpretable Machine Learning
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