Topics ›
3 articles RSS ↗
This paper introduces a self-supervised learning method for point cloud analysis using an upsampling autoencoder (UAE). The model uses subsampling and an encoder-decoder architecture to reconstruct the original point cloud, learning both semantic and geometric information. Experiments show the UAE outperforms existing methods in shape classification, part segmentation, and point cloud upsampling tasks.
arXiv · · CV Healthcare
This paper introduces a self-supervised contrastive learning method for segmenting the left ventricle in echocardiography images when limited labeled data is available. The approach uses contrastive pretraining to improve the performance of UNet and DeepLabV3 segmentation networks. Experiments on the EchoNet-Dynamic dataset show the method achieves a Dice score of 0.9252, outperforming existing approaches, with code available on Github.
MBZUAI · · Research Healthcare
Xiao Wang from Purdue University presented research on Adversarial Contrastive Learning (AdCo) and Cooperative-adversarial Contrastive Learning (CaCo) for improved self-supervised learning. He also discussed CryoREAD, a framework for building DNA/RNA structures from cryo-EM maps, and future work in deep learning for drug discovery. Wang's algorithms have impacted molecular biology, leading to new structure discoveries published in journals like Cell and Nature Microbiology. Why it matters: The research advances AI techniques for crucial tasks in molecular biology and drug discovery, with potential applications for institutions in the GCC region focused on healthcare and biotechnology.