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.

使用变分自动编码器进行监督学习的深度学习细胞特征提取模型，用于从多重免疫荧光细胞图像中提取细胞特征，并通过对乳腺癌患者的 1,093 个组织微阵列核心中提取的超过 44,000 个多重免疫荧光细胞图像块进行细胞表型分类来验证其成功。