  Deep learning systems have been proposed to improve the objectivity and
efficiency of Ki- 67 PI scoring. The challenge is that while very accurate,
deep learning techniques suffer from reduced performance when applied to
out-of-domain data. This is a critical challenge for clinical translation, as
models are typically trained using data available to the vendor, which is not
from the target domain. To address this challenge, this study proposes a domain
adaptation pipeline that employs an unsupervised framework to generate silver
standard (pseudo) labels in the target domain, which is used to augment the
gold standard (GS) source domain data. Five training regimes were tested on two
validated Ki-67 scoring architectures (UV-Net and piNET), (1) SS Only: trained
on target silver standard (SS) labels, (2) GS Only: trained on source GS
labels, (3) Mixed: trained on target SS and source GS labels, (4) GS+SS:
trained on source GS labels and fine-tuned on target SS labels, and our
proposed method (5) SS+GS: trained on source SS labels and fine-tuned on source
GS labels. The SS+GS method yielded significantly (p < 0.05) higher PI accuracy
(95.9%) and more consistent results compared to the GS Only model on target
data. Analysis of t-SNE plots showed features learned by the SS+GS models are
more aligned for source and target data, resulting in improved generalization.
The proposed pipeline provides an efficient method for learning the target
distribution without manual annotations, which are time-consuming and costly to
generate for medical images. This framework can be applied to any target site
as a per-laboratory calibration method, for widescale deployment.
