Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays

Recent advancements in extreme image compression have demonstrated that converting pixel data into highly compact latent representations can enhance coding efficiency. Traditional methods often rely on convolutional neural networks (CNNs) or Swin Transformers, which maintain significant spatial redundancy, limiting compression performance. The proposed Mixed RWKV-Transformer (MRT) architecture encodes images into compact 1-D latent representations by integrating the strengths of RWKV and Transformer models, capturing global dependencies and local redundancies effectively.

RiverScope is a newly developed high-resolution dataset aimed at improving the monitoring of rivers and surface water dynamics, which are crucial for understanding Earth's climate system. The dataset includes 1,145 high-resolution images covering 2,577 square kilometers, with expert-labeled river and surface water masks. This initiative addresses the challenges of monitoring narrow or sediment-rich rivers that are often inadequately represented in low-resolution satellite data.