机器学习 (ML) 已经发展成为计算机科学家专用的专业技术。除了一般的易用性之外，自动化管道还允许用最少的计算机科学知识来训练复杂的机器学习模型。近年来，自动化机器学习 (AutoML) 框架已成为专业机器学习模型的有力竞争对手，甚至能够在特定任务上超越后者。此外，这种成功不仅限于简单的任务，还包括复杂的任务，例如组织病理学组织中的肿瘤分割，这是一项非常耗时的任务，需要医疗专业人员多年的专业知识。在医学图像分割方面，领先的 AutoML 框架是 nnU-Net 和 deepflash2。在这项工作中，我们开始在组织病理学图像分割领域比较这两个框架。事实证明，这个用例特别具有挑战性，因为肿瘤和健康组织通常无法通过硬边界清晰地区分，而是通过异质过渡来区分。使用来自 56 名胶质母细胞瘤患者的 103 张完整幻灯片图像的数据集进行评估。培训和评估在带有消费类硬件的笔记本电脑上运行，确定框架是否适合临床场景而不是研究实验室的高性能场景。

Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring years of expertise by medical professionals. Regarding medical image segmentation, the leading AutoML frameworks are nnU-Net and deepflash2. In this work, we begin to compare those two frameworks in the area of histopathological image segmentation. This use case proves especially challenging, as tumor and healthy tissue are often not clearly distinguishable by hard borders but rather through heterogeneous transitions. A dataset of 103 whole-slide images from 56 glioblastoma patients was used for the evaluation. Training and evaluation were run on a notebook with consumer hardware, determining the suitability of the frameworks for their application in clinical scenarios rather than high-performance scenarios in research labs.