用75个研究的多通道显微图像训练通用细胞形态模型
CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images
- 用75个不同研究的异构多通道图像构建预训练数据集
- 模型在多通道生物成像任务中性能显著提升
- 适合需要跨模态分析的生物研究者使用
利用图像与机器学习量化细胞形态，已成为研究细胞对治疗反应的强大工具。然而，现有形态分析模型通常仅基于单一显微成像类型训练，导致模型专用性强，难以跨研究复用（如通道数不匹配）。本文提出CHAMMI-75，一个开放获取的数据集，包含来自75个不同生物研究的异构多通道显微图像。该数据集从公开资源整理而来，旨在构建可适应不同通道数的细胞形态模型。实验表明，使用CHAMMI-75进行预训练能显著提升多通道生物成像任务的性能，主要得益于其丰富的显微成像模态多样性。本工作为下一代生物研究用细胞形态模型奠定了基础。
原文摘要 · Abstract (English)
Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellular morphology are typically trained with a single microscopy imaging type. 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). Here, we present CHAMMI-75, an open access dataset of heterogeneous, multi-channel microscopy images from 75 diverse biological studies. We curated this resource from publicly available sources to investigate cellular morphology models that are channel-adaptive and can process any microscopy image type. Our experiments show that training with CHAMMI-75 can improve performance in multi-channel bioimaging tasks primarily because of its high diversity in microscopy modalities. This work paves the way to create the next generation of cellular morphology models for biological studies.
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