Computational pathology tasks have some unique characterises such as
multi-gigapixel images, tedious and frequently uncertain annotations, and
unavailability of large number of cases [13]. To address some of these issues,
we present Deep Fastfood Ensembles - a simple, fast and yet effective method
for combining deep features pooled from popular CNN models pre-trained on
totally different source domains (e.g., natural image objects) and projected
onto diverse dimensions using random projections, the so-called Fastfood [11].
The final ensemble output is obtained by a consensus of simple individual
classifiers, each of which is trained on a different collection of random basis
vectors. This offers extremely fast and yet effective solution, especially when
training times and domain labels are of the essence. We demonstrate the
effectiveness of the proposed deep fastfood ensemble learning as compared to
the state-of-the-art methods for three different tasks in histopathology image
analysis.Comment: arXiv admin note: substantial text overlap with arXiv:2104.0066