  Application of artificial intelligence in medicine brings in highly accurate
predictions achieved by complex models, the reasoning of which is hard to
interpret. Their generalization ability can be reduced because of the lack of
pixel wise annotated images that occurs in frozen section tissue analysis. To
partially overcome this gap, this paper explores the approximate explicit
feature map (aEFM) transform of low-dimensional data into a low-dimensional
subspace in Hilbert space. There, with a modest increase in computational
complexity, linear algorithms yield improved performance and keep
interpretability. They remain amenable to incremental learning that is not a
trivial issue for some nonlinear algorithms. We demonstrate proposed
methodology on a very large-scale problem related to intraoperative pixel-wise
semantic segmentation and clustering of adenocarcinoma of a colon in a liver.
Compared to the results in the input space, logistic classifier achieved
statistically significant performance improvements in micro balanced accuracy
and F1 score in the amounts of 12.04% and 12.58%, respectively. Support vector
machine classifier yielded the increase of 8.04% and 9.41%. For clustering,
increases of 0.79% and 0.85% are obtained with ultra large-scale spectral
clustering algorithm. Results are supported by a discussion of interpretability
using Shapely additive explanation values for predictions of linear classifier
in input space and aEFM induced space.
