Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images
This addresses the problem of efficient computational pathology analysis for researchers and clinicians by providing a more automated and effective patch selection method, though it appears incremental as it builds on existing divide-and-conquer approaches.
The paper tackles the challenge of selecting a small, representative subset of patches from gigapixel whole slide images in pathology by proposing the Selection of Distinct Morphologies (SDM) method, which demonstrates remarkable efficacy across datasets and eliminates the need for empirical parameterization compared to the state-of-the-art Yottixel's mosaic.
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.