Advances in automation and imaging have made it possible to capture a large image dataset that spans multiple experimental batches of data. However, accurate biological comparison across the batches is challenged by batch-to-batch variation (i.e. batch effect) due to uncontrollable experimental noise (e.g. varying stain intensity or cell density). Previous approaches to minimize the batch effect have commonly focused on normalizing the low-dimensional image measurements such as an embedding generated by a neural network. However, normalization of the embedding could suffer from over-correction and alter true biological features (e.g. cell size) due to our limited ability to interpret the effect of the normalization on the embedding space. Although techniques like flat-field correction can be applied to normalize the image values directly, they are limited transformations that handle only simple artifacts due to batch effect.

中文翻译：

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利用生成对抗网络进行批量均衡

自动化和成像技术的进步使得捕获跨越多个实验数据批次的大型图像数据集成为可能。但是，由于不可控制的实验噪声（例如，变化的染色强度或细胞密度），批次之间的差异（即批次效应）挑战了批次之间的准确生物学比较。使批次效应最小化的先前方法通常集中在归一化低维图像测量，例如由神经网络生成的嵌入。然而，由于我们解释归一化对嵌入空间的影响的能力有限，嵌入的归一化可能会遭受过度校正并改变真实的生物学特征（例如细胞大小）。尽管可以应用诸如平场校正之类的技术直接对图像值进行归一化，