  Advancements in digital imaging technologies have sparked increased interest
in using multiplexed immunofluorescence (mIF) images to visualise and identify
the interactions between specific immunophenotypes with the tumour
microenvironment at the cellular level. Current state-of-the-art multiplexed
immunofluorescence image analysis pipelines depend on cell feature
representations characterised by morphological and stain intensity-based
metrics generated using simple statistical and machine learning-based tools.
However, these methods are not capable of generating complex representations of
cells. We propose a deep learning-based cell feature extraction model using a
variational autoencoder with supervision using a latent subspace to extract
cell features in mIF images. We perform cell phenotype classification using a
cohort of more than 44,000 multiplexed immunofluorescence cell image patches
extracted across 1,093 tissue microarray cores of breast cancer patients, to
demonstrate the success of our model against current and alternative methods.
