Abstract: Medical image segmentation often involves inherent uncertainty due to inter observer variability. In this case, a single deterministic mask obtained by conventional segmentation networks, ...

Abstract: Deep learning models for medical image segmentation often struggle with task-specific characteristics, limiting their generalization to unseen tasks with new anatomies, labels, or modalities ...

Scientists have created an AI tool that could help doctors identify diseases quickly and accurately using only a small number of medical images. Credit: Victoria Kotlyarchuk/iStock A new artificial ...

The remarkable performance of large multimodal models (LMMs) has attracted significant interest from the image segmentation community. To align with the next-token-prediction paradigm, current ...

Semantic segmentation is an area of computer vision that specialises in dividing an image into regions based on pixel characteristics to identify objects or boundaries, simplifying image analysis. The ...

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques ...

The DoUnseen package segments and classifies novel objects in just few lines of code. Without any training or fine-tuning. To use the full segmentation pipeline, you need to install any zero-shot ...