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arXiv:2407.07171v1 Announce Type: new Abstract: The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate on employing consistency learning only for individual LiDAR representations. This narrow focus results in limited perturbations that generally fail to enable effective consistency learning. Additionally, these SSL approaches employ contrastive learning based on the sampling from a limited set of positive and negative embedding samples. This paper introduces a novel semi-supervised LiDAR semantic segmentation framework called ItTakesTwo (IT2). IT2 is designed to ensure…

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[Submitted on 26 Jun 2024] View a PDF of the paper titled Foundation Models for Electrocardiograms, by Junho Song and 5 other authors View PDF HTML (experimental) Abstract:Foundation models, enhanced by self-supervised learning (SSL) techniques, represent a cutting-edge frontier in biomedical signal analysis, particularly for electrocardiograms (ECGs), crucial for cardiac health monitoring and diagnosis. This study conducts a comprehensive analysis of foundation models for ECGs by employing and refining innovative SSL methodologies - namely, generative and contrastive learning - on a vast dataset of over 1.1 million ECG samples. By customizing these methods to align with the intricate characteristics of…