基于ConvNeXt和UPerNet的超声多任务模型，兼顾分割与疾病预测。
UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
- 融合ConvNeXt与UPerNet，统一处理超声图像分割与分类任务
- 在超9700张标注图像上训练，多个数据集达顶尖性能
- 计算开销更低，适合临床实时应用，开源代码可获取
超声成像因成本低、便携、安全而广泛应用于临床。然而，当前人工智能研究通常将疾病预测与组织分割视为独立任务，且模型计算开销大。为此，我们提出UltraUPConvNet，一种计算高效的通用框架，用于同时完成超声图像分类与分割。该模型在包含超过9,700个标注、覆盖七个解剖区域的大规模数据集上训练，部分数据集上达到最新技术水平，且计算资源消耗更低。模型权重与代码已公开于https://github.com/yyxl123/UltraUPConvNet。
原文摘要 · Abstract (English)
Ultrasound imaging is widely used in clinical practice due to its cost-effectiveness, mobility, and safety. However, current AI research often treats disease prediction and tissue segmentation as two separate tasks and their model requires substantial computational overhead. In such a situation, we introduce UltraUPConvNet, a computationally efficient universal framework designed for both ultrasound image classification and segmentation. Trained on a large-scale dataset containing more than 9,700 annotations across seven different anatomical regions, our model achieves state-of-the-art performance on certain datasets with lower computational overhead. Our model weights and codes are available at https://github.com/yyxl123/UltraUPConvNet
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。