  Domain shift is a problem commonly encountered when developing automated
histopathology pipelines. The performance of machine learning models such as
convolutional neural networks within automated histopathology pipelines is
often diminished when applying them to novel data domains due to factors
arising from differing staining and scanning protocols. The Dual-Channel
Auto-Encoder (DCAE) model was previously shown to produce feature
representations that are less sensitive to appearance variation introduced by
different digital slide scanners. In this work, the Multi-Channel Auto-Encoder
(MCAE) model is presented as an extension to DCAE which learns from more than
two domains of data. Additionally, a synthetic dataset is generated using
CycleGANs that contains aligned tissue images that have had their appearance
synthetically modified. Experimental results show that the MCAE model produces
feature representations that are less sensitive to inter-domain variations than
the comparative StaNoSA method when tested on the novel synthetic data.
Additionally, the MCAE and StaNoSA models are tested on a novel tissue
classification task. The results of this experiment show the MCAE model out
performs the StaNoSA model by 5 percentage-points in the f1-score. These
results show that the MCAE model is able to generalise better to novel data and
tasks than existing approaches by actively learning normalised feature
representations.
