Professor Yinyin Yuan’s Team works to reconstruct two-dimensional or three-dimensional tumour models from images of tumour sections.
Professor Yuan joined the ICR in 2012 as the leader of the Computational Pathology and Integrative Genomics team. Her team uses techniques from a broad range of scientific fields to formulate unique approaches for linking genetic mutations, pathological observations and patient treatment to improve cancer research.
Trained as an R&D engineer, computer vision and machine learning, Khalid studies the evolution of lung cancers by integrating omics data with digital pathology.
Trained in medical image analysis and machine learning, Yeman develops histology and omics integrative computational methods to understand pre-cancer evolution.
Azam employs deep learning and image processing to study childhood rhabdomyosarcoma tumour microenvironment.
Trained in digital pathology, MRI Image analytics and machine learning, Priya brings experiences from industry to study breast cancers as complex ecological systems.
Faranak uses deep learning to predict which breast cancer progresses from precursor lesion to invasive stage for improved cancer management.
Hanyun applies ecological concepts in a pan-cancer analysis of immune cell heterogeneity.
Konstantinos combines MRI and digital pathology to study childhood neuroblastoma.