CHAMMI-75

Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. However, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), or because the target experimental conditions are out of distribution. We have created CHAMMI-75, a large-scale dat...借助图像与机器学习模型量化细胞形态，已被证实是研究细胞对实验处理响应的有力工具。然而，当前用于量化细胞形态的模型通常仅基于单一显微成像类型，并在受控实验条件下训练所得。此类模型仅具有专用性，无法在不同生物学研究中复用：究其缘由，要么是技术规格不匹配（例如成像通道（channels）数量存在差异），要么是目标实验条件属于模型的分布外样本。我们构建了CHAMMI-75这一大型数...