Semi-supervised variational autoencoder for cell feature extraction in multiplexed immunofluorescence images

TL;DR

This paper introduces a semi-supervised variational autoencoder model that extracts complex cell features from multiplexed immunofluorescence images, improving cell phenotype classification in breast cancer tissue analysis.

Contribution

It presents a novel deep learning model that combines supervision with a variational autoencoder to generate detailed cell representations from mIF images.

Findings

Outperforms existing methods in cell phenotype classification.

Successfully extracts complex cell features from large mIF datasets.

Demonstrates applicability in breast cancer tissue microarrays.

Abstract

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.