  We consider the problem of integrating non-imaging information into
segmentation networks to improve performance. Conditioning layers such as FiLM
provide the means to selectively amplify or suppress the contribution of
different feature maps in a linear fashion. However, spatial dependency is
difficult to learn within a convolutional paradigm. In this paper, we propose a
mechanism to allow for spatial localisation conditioned on non-imaging
information, using a feature-wise attention mechanism comprising a
differentiable parametrised function (e.g. Gaussian), prior to applying the
feature-wise modulation. We name our method INstance modulation with SpatIal
DEpendency (INSIDE). The conditioning information might comprise any factors
that relate to spatial or spatio-temporal information such as lesion location,
size, and cardiac cycle phase. Our method can be trained end-to-end and does
not require additional supervision. We evaluate the method on two datasets: a
new CLEVR-Seg dataset where we segment objects based on location, and the ACDC
dataset conditioned on cardiac phase and slice location within the volume. Code
and the CLEVR-Seg dataset are available at https://github.com/jacenkow/inside.
