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BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation
Segmenting bruises is a challenging task in medical imaging due to limited data and annotations, diffuse boundaries, and highly variable appearance. In this work, we propose BruNet, a segmentation framework that combines a ViT-based visual encoder (a self-supervised DINOv3 or a pretrained LingBot-Vision backbone) with a SAM-based mask decoder. BruNet is trained on the HAM10000 skin lesion dataset and evaluated on a separate bruise dataset without additional fine-tuning. Although a small number of prior studies have explored machine learning and computer vision for bruise analysis, existing wor
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T12:35:20.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.