OpenUS: A Fully Open-Source Foundation Model for Ultrasound Image Analysis via Self-Adaptive Masked Contrastive Learning

A recent study titled 'Are Emotions Arranged in a Circle?' explores the geometric analysis of emotion representations through hyperspherical contrastive learning, proposing a method to align emotions in a circular format within language model embeddings. This approach aims to enhance interpretability and robustness against dimensionality reduction, although it shows limitations in high-dimensional settings and fine-grained classification tasks.

A novel framework named DINO-AugSeg has been proposed to enhance few-shot medical image segmentation by leveraging DINOv3-based self-supervised features. This approach addresses the challenge of limited annotated training data in clinical settings, utilizing wavelet-based feature-level augmentation and contextual information-guided fusion to improve segmentation accuracy across various imaging modalities such as MRI and CT.