FIG 3

Inference demonstrates that mimicry embedding and trained CapsNet allow efficient classification of VACV particles into four biological classes. (A) Merged four-channel fluorescent image of a HeLa cell infected with VACV previously unseen by CapsNet (see Fig. S1A for channel details). Bar, 10 μm. (B) Respective ZedMate particle detection and classification by conventional binning of fluorescence intensities. (C) Respective inference of cell-free and cell-associated particles detected by ZedMate, mimicry embedded and predicted by a trained CapsNet (Fig. 2B and C). (D) Combined ZedMate particle detection with mimicry-embedded and trained CapsNet results in classification of four types of biologically relevant VACV particles. (Insets) Quantification of the particle types in the respective image. Statistical validation of machine learning models is provided in Fig. S3.