  The difficulty of processing gigapixel whole slide images (WSIs) in clinical
microscopy has been a long-standing barrier to implementing computer aided
diagnostic systems. Since modern computing resources are unable to perform
computations at this extremely large scale, current state of the art methods
utilize patch-based processing to preserve the resolution of WSIs. However,
these methods are often resource intensive and make significant compromises on
processing time. In this paper, we demonstrate that conventional patch-based
processing is redundant for certain WSI classification tasks where high
resolution is only required in a minority of cases. This reflects what is
observed in clinical practice; where a pathologist may screen slides using a
low power objective and only switch to a high power in cases where they are
uncertain about their findings. To eliminate these redundancies, we propose a
method for the selective use of high resolution processing based on the
confidence of predictions on downscaled WSIs --- we call this the Selective
Objective Switch (SOS). Our method is validated on a novel dataset of 684
Liver-Kidney-Stomach immunofluorescence WSIs routinely used in the
investigation of autoimmune liver disease. By limiting high resolution
processing to cases which cannot be classified confidently at low resolution,
we maintain the accuracy of patch-level analysis whilst reducing the inference
time by a factor of 7.74.
