提出新评估方法，让荧光显微图像模型更懂分布外泛化。
Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopy
- 用JUMP-CP数据集隔离分布偏移源，精准评估泛化能力。
- 提出共享解码器的通道无关自编码器，支持多荧光标记扩展。
- 在不同实验批次、药物和荧光标记上均表现鲁棒，适合跨细胞类型迁移。
高内涵筛选中的计算机视觉发展面临多重分布偏移挑战，如实验条件、处理剂和荧光标记的变化。传统基于迁移学习的模型评估常混杂多种偏移源，难以解析模型设计与训练对泛化的影响。本文提出一种基于JUMP-CP数据集的评估方案，可分离不同分布偏移来源，实现针对特定偏移源的泛化评估。进一步提出通道无关的掩码自编码器$ extbf{Campfire}$，通过共享解码器适配多种荧光标记，有效扩展至多标记数据集。结果表明，该模型在分布外的实验批次、处理剂及荧光标记上均具优异泛化性能，并成功实现从一种细胞类型到另一种的迁移学习。
原文摘要 · Abstract (English)
Developing computer vision for high-content screening is challenging due to various sources of distribution-shift caused by changes in experimental conditions, perturbagens, and fluorescent markers. The impact of different sources of distribution-shift are confounded in typical evaluations of models based on transfer learning, which limits interpretations of how changes to model design and training affect generalisation. We propose an evaluation scheme that isolates sources of distribution-shift using the JUMP-CP dataset, allowing researchers to evaluate generalisation with respect to specific sources of distribution-shift. We then present a channel-agnostic masked autoencoder $\mathbf{Campfire}$ which, via a shared decoder for all channels, scales effectively to datasets containing many different fluorescent markers, and show that it generalises to out-of-distribution experimental batches, perturbagens, and fluorescent markers, and also demonstrates successful transfer learning from one cell type to another.
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