  Traction Force Microscopy (TFM) is a technique used to determine the tensions
that a biological cell conveys to the underlying surface. Typically, TFM
requires culturing cells on gels with fluorescent beads, followed by bead
displacement calculations. We present a new method allowing to predict those
forces from a regular fluorescent image of the cell. Using Deep Learning, we
trained a Bayesian Neural Network adapted for pixel regression of the forces
and show that it generalises on different cells of the same strain. The
predicted forces are computed along with an approximated uncertainty, which
shows whether the prediction is trustworthy or not. Using the proposed method
could help estimating forces when calculating non-trivial bead displacements
and can also free one of the fluorescent channels of the microscope. Code is
available at \url{https://github.com/wahlby-lab/InSilicoTFM}.
