论文标题
Miseval：用于医学图像分割评估的公制库
MISeval: a Metric Library for Medical Image Segmentation Evaluation
论文作者
论文摘要
正确的绩效评估对于评估医学中的现代人工智能算法（例如基于深度学习的医学图像分割模型）至关重要。但是，Python中没有通用度量库可用于标准化和可再现的评估。因此，我们提出了我们的开放源代码Python软件包Misesval：用于医疗图像细分评估的度量库。实施的指标可以直观地使用并轻松整合到任何性能评估管道中。该软件包利用现代CI/CD策略来确保功能和稳定性。 Miseval可从PYPI（Miseval）和Github获得：https：//github.com/frankkramer-lab/miseval。
Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However, there is no universal metric library in Python for standardized and reproducible evaluation. Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation. The implemented metrics can be intuitively used and easily integrated into any performance assessment pipeline. The package utilizes modern CI/CD strategies to ensure functionality and stability. MISeval is available from PyPI (miseval) and GitHub: https://github.com/frankkramer-lab/miseval.
