Selection of Distinct Morphologies to Divide & Conquer Gigapixel
  Pathology Images
Abubakr Shafique Saghir Alfasly Areej Alsaafin Peyman Nejat Jibran A. Khan H. R. Tizhoosh
Whole slide images (WSIs) are massive digital pathology files illustrating
intricate tissue structures. Selecting a small, representative subset of
patches from each WSI is essential yet challenging. Therefore, following the
"Divide & Conquer" approach becomes essential to facilitate WSI analysis
including the classification and the WSI matching in computational pathology.
To this end, we propose a novel method termed "Selection of Distinct
Morphologies" (SDM) to choose a subset of WSI patches. The aim is to encompass
all inherent morphological variations within a given WSI while simultaneously
minimizing the number of selected patches to represent these variations,
ensuring a compact yet comprehensive set of patches. This systematically
curated patch set forms what we term a "montage". We assess the
representativeness of the SDM montage across various public and private
histopathology datasets. This is conducted by using the leave-one-out WSI
search and matching evaluation method, comparing it with the state-of-the-art
Yottixel's mosaic. SDM demonstrates remarkable efficacy across all datasets
during its evaluation. Furthermore, SDM eliminates the necessity for empirical
parameterization, a crucial aspect of Yottixel's mosaic, by inherently
optimizing the selection process to capture the distinct morphological features
within the WSI.