Reproduce “Self-supervised dense representation learning for live-cell microscopy with time arrow prediction” on OpenResearch

About this paper

Benjamin Gallusser, Max Stieber, Martin Weigert

Researchers at EPFL developed TAP (Time Arrow Prediction), a self-supervised learning scheme that generates dense, pixel-level representations from unlabeled live-cell microscopy videos by predicting the temporal order of shuffled image frames. This approach significantly reduces the need for manual annotations, outperforming supervised baselines in low-data scenarios for various downstream tasks, including cell state classification and mitosis segmentation.

Key insights

• Biological processes like cell division exhibit strong time asymmetry that can be effectively learned by a self-supervised model, while static background regions provide no such cues.
• Incorporating a permutation-equivariant prediction head significantly improves training stability by embedding an inductive bias consistent with the time arrow prediction task's inherent symmetry.
• Carefully selected data augmentations are essential to force the model to learn robust, biologically meaningful features instead of spurious correlations with temporal order.

Summary by alphaXiv.