  Automating the classification of camera-obtained microscopic images of White
Blood Cells (WBCs) and related cell subtypes has assumed importance since it
aids the laborious manual process of review and diagnosis. Several
State-Of-The-Art (SOTA) methods developed using Deep Convolutional Neural
Networks suffer from the problem of domain shift - severe performance
degradation when they are tested on data (target) obtained in a setting
different from that of the training (source). The change in the target data
might be caused by factors such as differences in camera/microscope types,
lenses, lighting-conditions etc. This problem can potentially be solved using
Unsupervised Domain Adaptation (UDA) techniques albeit standard algorithms
presuppose the existence of a sufficient amount of unlabelled target data which
is not always the case with medical images. In this paper, we propose a method
for UDA that is devoid of the need for target data. Given a test image from the
target data, we obtain its 'closest-clone' from the source data that is used as
a proxy in the classifier. We prove the existence of such a clone given that
infinite number of data points can be sampled from the source distribution. We
propose a method in which a latent-variable generative model based on
variational inference is used to simultaneously sample and find the
'closest-clone' from the source distribution through an optimization procedure
in the latent space. We demonstrate the efficacy of the proposed method over
several SOTA UDA methods for WBC classification on datasets captured using
different imaging modalities under multiple settings.
