论文标题
使用多尺度特征提取的高效，高性能胰腺分割
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction
论文作者
论文摘要
为了基于人工智能的图像分析方法达到临床适用性，高性能算法的发展至关重要。例如，基于自然图像的存在分割算法既不有效地使用参数使用，也不是用于医学成像的优化。在这里，我们提出了Monet，这是一种高度优化的基于神经网络的胰腺分割算法，旨在通过有效的多尺度图像特征利用率来实现高性能。
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial. For example, existent segmentation algorithms based on natural images are neither efficient in their parameter use nor optimized for medical imaging. Here we present MoNet, a highly optimized neural-network-based pancreatic segmentation algorithm focused on achieving high performance by efficient multi-scale image feature utilization.
