Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

This paper proposes a new active learning method for semantic segmentation. The core of
our method lies in a new annotation query design. It samples informative local image …

Recently, advancements in deep learning-based superpixel segmentation methods have
brought about improvements in both the efficiency and the performance of segmentation …

Non-destructive 3D imaging techniques, such as X-ray nano-holo-tomography, enable the
visualization of battery electrodes. Segmenting electrodes into distinct phases is crucial for a …

Abstract Unsupervised Domain Adaptation (UDA) for semantic segmentation has been
widely studied to exploit the label-rich source data to assist the segmentation of unlabeled …

Training and validating models for semantic segmentation require datasets with pixel-wise
annotations, which are notoriously labor-intensive. Although useful priors such as …

Vision-language models (VLMs) have demonstrated remarkable zero-shot performance
across various classification tasks. Nonetheless, their reliance on hand-crafted text prompts …