  Segmentation of ultra-high resolution images is challenging because of their
enormous size, consisting of millions or even billions of pixels. Typical
solutions include dividing input images into patches of fixed size and/or
down-sampling to meet memory constraints. Such operations incur information
loss in the field-of-view (FoV) i.e., spatial coverage and the image
resolution. The impact on segmentation performance is, however, as yet
understudied. In this work, we start with a motivational experiment which
demonstrates that the trade-off between FoV and resolution affects the
segmentation performance on ultra-high resolution images---and furthermore, its
influence also varies spatially according to the local patterns in different
areas. We then introduce foveation module, a learnable "dataloader" which, for
a given ultra-high resolution image, adaptively chooses the appropriate
configuration (FoV/resolution trade-off) of the input patch to feed to the
downstream segmentation model at each spatial location of the image. The
foveation module is jointly trained with the segmentation network to maximise
the task performance. We demonstrate on three publicly available
high-resolution image datasets that the foveation module consistently improves
segmentation performance over the cases trained with patches of fixed
FoV/resolution trade-off. Our approach achieves the SoTA performance on the
DeepGlobe aerial image dataset. On the Gleason2019 histopathology dataset, our
model achieves better segmentation accuracy for the two most clinically
important and ambiguous classes (Gleason Grade 3 and 4) than the top performers
in the challenge by 13.1% and 7.5%, and improves on the average performance of
6 human experts by 6.5% and 7.5%. Our code and trained models are available at
$\text{https://github.com/lxasqjc/Foveation-Segmentation}$.
