论文标题
跨医学学科的标准化医学图像分类
Standardized Medical Image Classification across Medical Disciplines
论文作者
论文摘要
Aucmedi是用于医学图像分类的基于Python的框架。在本文中，我们通过将其应用于多个数据集来评估AUCMedi的功能。专门选择数据集以涵盖各种医学学科和成像方式。我们使用jupyter笔记本设计了一个简单的管道，并将其应用于所有数据集。结果表明，AUCMedi能够为每个数据集具有准确的分类功能训练模型：每个数据集的AUC范围为0.82至1.0，平均F1分数在0.61和1.0之间。 Aucmedi具有高适应性和强大的性能，被证明是建立广泛适用神经网络的强大工具。这些笔记本是Aucmedi的申请示例。
AUCMEDI is a Python-based framework for medical image classification. In this paper, we evaluate the capabilities of AUCMEDI, by applying it to multiple datasets. Datasets were specifically chosen to cover a variety of medical disciplines and imaging modalities. We designed a simple pipeline using Jupyter notebooks and applied it to all datasets. Results show that AUCMEDI was able to train a model with accurate classification capabilities for each dataset: Averaged AUC per dataset range between 0.82 and 1.0, averaged F1 scores range between 0.61 and 1.0. With its high adaptability and strong performance, AUCMEDI proves to be a powerful instrument to build widely applicable neural networks. The notebooks serve as application examples for AUCMEDI.
