Breast Ultrasound
Breast ultrasound is a crucial diagnostic tool for detecting and characterizing breast lesions, with research focusing on improving the accuracy and efficiency of image analysis. Current efforts utilize deep learning models, including U-Net variations, transformers (like ViT), and novel architectures like Mamba-based networks, often incorporating techniques like contrastive learning and knowledge distillation to enhance performance with limited labeled data. These advancements aim to improve the speed and accuracy of breast cancer diagnosis, potentially assisting radiologists and leading to earlier and more effective interventions.
Papers
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