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, ...

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 ...

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 ...

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 ...

In recent years, the exploitation of three-dimensional (3D) data in deep learning has gained momentum despite its inherent challenges. The necessity of 3D approaches arises from the limitations of two ...

Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish ...

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 ...