LiMT: A Multi-task Liver Image Benchmark Dataset

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.

A recent study evaluated the performance, efficiency, and accessibility of automated machine learning (AutoML) frameworks in the field of radiomics, focusing on their ability to assist researchers without programming skills in developing predictive models. The study tested six general-purpose and five radiomics-specific frameworks across ten diverse datasets, revealing the need for further development tailored to radiomics challenges.