  The shift towards end-to-end deep learning has brought unprecedented advances
in many areas of computer vision. However, deep neural networks are trained on
images with resolutions that rarely exceed $1,000 \times 1,000$ pixels. The
growing use of scanners that create images with extremely high resolutions
(average can be $100,000 \times 100,000$ pixels) thereby presents novel
challenges to the field. Most of the published methods preprocess
high-resolution images into a set of smaller patches, imposing an a priori
belief on the best properties of the extracted patches (magnification, field of
view, location, etc.). Herein, we introduce Magnifying Networks (MagNets) as an
alternative deep learning solution for gigapixel image analysis that does not
rely on a preprocessing stage nor requires the processing of billions of
pixels. MagNets can learn to dynamically retrieve any part of a gigapixel
image, at any magnification level and field of view, in an end-to-end fashion
with minimal ground truth (a single global, slide-level label). Our results on
the publicly available Camelyon16 and Camelyon17 datasets corroborate to the
effectiveness and efficiency of MagNets and the proposed optimization framework
for whole slide image classification. Importantly, MagNets process far less
patches from each slide than any of the existing approaches ($10$ to $300$
times less).
