Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations.

We study detecting cell events in phase-contrast microscopy sequences from few annotations. We first detect event candidates from the intensity difference of consecutive frames, and then train an unsupervised novelty detector on these candidates. The novelty detector assigns each candidate a degree of surprise. We annotate a tiny number of candidates chosen according to the novelty detector's output, and finally train a sparse Gaussian process (GP) classifier. We show that the steepest learning curve is achieved when a collaborative multi-output Gaussian process is used as novelty detector, and its predictive mean and variance are used together to measure the degree of surprise. Following this scheme, we closely approximate the fully-supervised event detection accuracy by annotating only 3% of all candidates. The novelty detector based annotation used here clearly outperforms the studied active learning based approaches.