  While the use of artificial intelligence (AI) for medical image analysis is
gaining wide acceptance, the expertise, time and cost required to generate
annotated data in the medical field are significantly high, due to limited
availability of both data and expert annotation. Strongly supervised object
localization models require data that is exhaustively annotated, meaning all
objects of interest in an image are identified. This is difficult to achieve
and verify for medical images. We present a method for the transformation of
real data to train any Deep Neural Network to solve the above problems. We show
the efficacy of this approach on both a weakly supervised localization model
and a strongly supervised localization model. For the weakly supervised model,
we show that the localization accuracy increases significantly using the
generated data. For the strongly supervised model, this approach overcomes the
need for exhaustive annotation on real images. In the latter model, we show
that the accuracy, when trained with generated images, closely parallels the
accuracy when trained with exhaustively annotated real images. The results are
demonstrated on images of human urine samples obtained using microscopy.
