qPaLM: quantifying occult microarchitectural relationships in histopathological landscapes
Kendall, T. J.; Duff, C. M.; Thomson, A. M.; Iredale, J. P.
Show abstract
Optimal tissue imaging methods should be easy to apply, not require use-specific algorithmic training, and should leverage feature relationships central to subjective gold-standard assessment. We reinterpret histological images as landscapes to describe quantitative pathological landscape metrics (qPaLM), a generalisable framework defining topographic relationships in tissue using geoscience approaches. qPaLM requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification.
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