Resource-Limited Automated Ki67 Index Estimation in Breast Cancer
J. Gliozzo G. Marinò A. Bonometti M. Frasca D. Malchiodi
The prediction of tumor progression and chemotherapy response has been
recently tackled exploiting Tumor Infiltrating Lymphocytes (TILs) and the
nuclear protein Ki67 as prognostic factors. Recently, deep neural networks
(DNNs) have been shown to achieve top results in estimating Ki67 expression and
simultaneous determination of intratumoral TILs score in breast cancer cells.
However, in the last ten years the extraordinary progress induced by deep
models proliferated at least as much as their resource demand. The exorbitant
computational costs required to query (and in some cases also to store) a deep
model represent a strong limitation in resource-limited contexts, like that of
IoT-based applications to support healthcare personnel. To this end, we propose
a resource consumption-aware DNN for the effective estimate of the percentage
of Ki67-positive cells in breast cancer screenings. Our approach reduced up to
75% and 89% the usage of memory and disk space respectively, up to 1.5x the
energy consumption, and preserved or improved the overall accuracy of a
benchmark state-of-the-art solution. Encouraged by such positive results, we
developed and structured the adopted framework so as to allow its general
purpose usage, along with a public software repository to support its usage.