用时间方向预测提升细胞事件识别，少标注也能高精度。
Self-supervised Representation Learning for Cell Event Recognition through Time Arrow Prediction
- 通过预测时间流向学习细胞图像特征表示。
- 少标注情况下优于端到端监督训练模型。
- 适用于生物显微图像分析，适合资源有限的研究者。
活细胞显微图像具有时空特性，对细胞状态分析构成挑战，而基于深度学习的分割或追踪方法通常依赖大量高质量标注数据。本文探索了一种替代方案：利用时间方向预测（TAP）进行自监督表征学习（SSRL），提取特征图用于下游的细胞事件识别任务。通过大量实验与分析，结果表明该方法在标注数据有限的情况下，性能优于完全监督的端到端训练模型。同时，研究为TAP在活细胞显微成像中的应用提供了深入洞察。
原文摘要 · Abstract (English)
The spatio-temporal nature of live-cell microscopy data poses challenges in the analysis of cell states which is fundamental in bioimaging. Deep-learning based segmentation or tracking methods rely on large amount of high quality annotations to work effectively. In this work, we explore an alternative solution: using feature maps obtained from self-supervised representation learning (SSRL) on time arrow prediction (TAP) for the downstream supervised task of cell event recognition. We demonstrate through extensive experiments and analysis that this approach can achieve better performance with limited annotation compared to models trained from end to end using fully supervised approach. Our analysis also provides insight into applications of the SSRL using TAP in live-cell microscopy.
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