FIG 2

FIG 2

Mimicry embedding allows separation of cell-free and cell-associated VACV particles through weights transfer from a CapsNet trained on the binary MNIST data set. (A) CapsNet architecture for training on the MNIST handwritten digits data set repurposed into a binary classification problem (<5 or ≥5) prior to CapsNet weights transfer. Black numbers represent dimensions of tensors. ReLU, rectified linear unit. (B) Mimicry embedding of VACV Z-profiles detected by ZedMate. The intensity matrix of fluorescence signal (Fig. 1) was embedded to mimic MNIST data using linear interpolation and padding. Bar, 1 μm. CapsNet architecture with pretrained weights from A was used for training on mimicry-embedded VACV particles. (C) Reconstructed particle profiles of the virions separated into cell-free and cell-associated subsets by CapsNet. (D) Representative mimicry-embedded VACV particles for comparison to images in panel C. Statistical validation of machine learning models is provided in Fig. S3.