ArXiv Preprint
Image analysis and machine learning algorithms operating on multi-gigapixel
whole-slide images (WSIs) often process a large number of tiles (sub-images)
and require aggregating predictions from the tiles in order to predict
WSI-level labels. In this paper, we present a review of existing literature on
various types of aggregation methods with a view to help guide future research
in the area of computational pathology (CPath). We propose a general CPath
workflow with three pathways that consider multiple levels and types of data
and the nature of computation to analyse WSIs for predictive modelling. We
categorize aggregation methods according to the context and representation of
the data, features of computational modules and CPath use cases. We compare and
contrast different methods based on the principle of multiple instance
learning, perhaps the most commonly used aggregation method, covering a wide
range of CPath literature. To provide a fair comparison, we consider a specific
WSI-level prediction task and compare various aggregation methods for that
task. Finally, we conclude with a list of objectives and desirable attributes
of aggregation methods in general, pros and cons of the various approaches,
some recommendations and possible future directions.
Mohsin Bilal, Robert Jewsbury, Ruoyu Wang, Hammam M. AlGhamdi, Amina Asif, Mark Eastwood, Nasir Rajpoot
2022-11-02
