Image-Omics The Yuan Lab Publications Team Projects Contact

Evolving pathology
with AI

Missions


Advancing digital pathology with AI

We apply machine learning and computer vision innovation to pathology for more accurate prediction of cancer survival and treatment response.

Understanding tumours as evolving ecosystems

Our scientific focus is on understanding tumours as complex, evolving ecosystems that may govern the evolutionary trajectory of cancer.

Deciphering cancer treatment resistance with image-omics

Innovative integration of pathological images with next-generation sequencing offers new insights into why some cancers are so difficult to treat.

Publications


Topological Tumor Graphs
Topological Tumor Graphs
TRACERx
TRACERx
SuperCRF
SuperCRF
Spatial niche
Spatial niche
ConcordeNet
ConcordeNet
DCIS ecology
DCIS ecology
Immune Hotspots
Immune Hotspots
Ecological Diversity Index
Ecological diversity

The Team


Yinyin

Yinyin Yuan

Team leader

Yinyin brings 10 years of experience in digital pathology and 15 years in machine learning to cancer research.

Priya

Priya L. Narayanan

Postdoc

Trained in digital pathology, MRI Image analytics and machine learning, Priya brings experiences from industry to study breast cancers as complex ecological systems.

Khalid AbdulJabbar

Postdoc

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.

Yeman Brhane Hagos

Yeman Brhane Hagos

PhD student

Trained in medical image analysis and machine learning, Yeman develops histology and omics integrative computational methods to understand pre-cancer evolution.

Sathya

Sathya Muralidha

Postdoc

Sathya applies her expertise in bioinformatics to integrate image-omics to decipher tumour heterogeneity

Faranak Sobhani

Postdoc

Faranak uses deep learning to predict which breast cancer progresses from precursor lesion to invasive stage for improved cancer management.

Azam

Azam Hamidinekoo

Postdoc

Azam employs deep learning and image processing to study childhood rhabdomyosarcoma tumour microenvironment.

Hanyun Zhang

Hanyun Zhang

PhD student

Hanyun applies ecological concepts in a pan-cancer analysis of immune cell heterogeneity.

Justin Law

Justin Law

PhD student

Justin uses deep learning to study Barrett's Oesophagus.

Xiaoxi Pan

Xiaoxi Pan

Postdoc

Xiaoxi brings experience from computer vision and medical image analysis to lung cancer evolution study with digital pathology.

Anca Grapa

Anca Grapa

Postdoc

Anca applies signal/image processing and machine learning to the study of lung cancer evolution, in a digital pathology framework.

Simon Castillo

Simon Castillo

Postdoc

Hailing from Chile and trained in Ecology, Simon seeks to understand brain tumour ecosystems while diving into de ocean of deep learning.

Alumni


Andreas Heindl

Now at Kheiron Medical

Shan Raza

Now faculty at Warwick University

Henrik Failmezger

Now at Roche

Konstantinos Zormpas-Petridis

Now postdoc at ICR

Lab retreat

Contact

Centre for Evolution and Cancer, & Division for Molecular Pathology,
The Institute of Cancer Research, London

Brookes Lawley Building,
The Institute of Cancer Research, London, 15 Cotswold Road, Sutton, Surrey, UK, SM2 5NG