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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- PossiblePossibly related (embedding) · 46%Systematic Review Weighs Machine Learning for Acute Ischemic Stroke Segmentation - Bioengineer.org →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
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- LinkedLinked via arxiv author · 85%Qiming Wang →
“BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation”
- LinkedLinked via arxiv author · 85%Richard J. Motley →
“BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation”
- LinkedLinked via arxiv author · 85%Ebube E. Obi →
“BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation”