The NuCLS dataset contains over 220.000 annotations of cell nuclei in breast cancers. We show how to use these data to create a multi-rater model with the MIScnn Framework to automate the analysis of cell nuclei. For the model creation, we use the widespread U-Net approach embedded in a pipeline. This pipeline provides besides the high performance convolution neural network, several preprocessor techniques and a extended data exploration. The final model is tested in the evaluation phase using a wide variety of metrics with a subsequent visualization. Finally, the results are compared and interpreted with the results of the NuCLS study. As an outlook, indications are given which are important for the future development of models in the context of cell nuclei.

翻译：NuCLS数据集包含超过22万个乳腺癌细胞核标注。我们展示了如何利用这些数据，通过MIScnn框架构建多评估者模型，以实现细胞核的自动化分析。在模型构建过程中，我们采用了嵌入流水线中的广泛使用的U-Net架构。该流水线除了提供高性能卷积神经网络外，还包含多种预处理技术及扩展的数据探索功能。最终模型在评估阶段通过多种指标进行测试，并进行了可视化呈现。最后，将结果与NuCLS研究的结果进行比较与解读。作为展望，本文指出了未来细胞核相关模型开发中需要关注的重要方向。