Approaches for Handling Immunopathological and Clinical Data Using Deep Learning Methodology: Multiplex IHC/IF Data as a Paradigm
Siting Goh, Yueda Chua, Justina Lee, Joe Poh-Sheng Yeong, Yiyu Cai · IntechOpen eBooks · 2021
Recent advancements in deep learning based artificial intelligence have enabled us to analyse complex data in order to provide patients with improved cancer prognosis, which is an important goal in precision health medicine. In this chapter, we would be discussing how deep learning could be applied to clinical data and immunopathological images to accurately determine survival rate prediction for patients. Multiplex immunohistochemistry/immunofluorescence (mIHC/IF) is a relatively new technology for simultaneous detection of multiple specific proteins from a single tissue section. To adopt deep learning, we collected and pre-processed the clinical and mIHC/IF data from a group of patients into three branches of data. These data were subsequently used to train and validate a neural network. The specific process and our recommendations will be further discussed in this chapter. We believe that our work will help the community to better handle their data for AI implementation while improving its performance and accuracy.