Dr Vivek Singh

BEng, MEng, PhD
Lecturer in Digital Pathology
Group Leader
Research Focus

Our research group focuses on identifying imaging-based biomarkers. We integrate diverse forms of data - such as medical imaging, genomic data and patient health records – to develop robust AI tools that aid early diagnosis across a range of cancer types. The ultimate aim is translating our research and building tools for cancer diagnosis.

Key Publications
  • True-T–Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images. Computational and Structural Biotechnology Journal. 23 (2024): 174-185. PMID: 38146436
  • COMFormer: classification of maternal-fetal and brain anatomy using a residual cross-covariance attention guided transformer in ultrasound. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2023). PMID: 37665699
  • Current and emerging trends in medical image segmentation with deep learning. IEEE Transactions on Radiation and Plasma Medical Sciences. (2023) 7(6) 545 - 569. doi: 10.1109/TRPMS.2023.3265863.
  • Prior wavelet knowledge for multi-modal medical image segmentation using a lightweight neural network with attention guided features.(2022) 209: 118166. doi: 10.1016/j.eswa.2022.118166
  • ICOSeg: Real-Time ICOS Protein Expression Segmentation from Immunohistochemistry Slides Using a Lightweight Conv-Transformer Network. Cancers. 14(16):3910 (2022): 3910. PMID: 36010903
Major Funding
Barts Charity Precision Medicine Programme Part 2 Lectureship
Dr Vivek Kumar Singh serves as a Lecturer specializing in Artificial Intelligence and Digital Pathology at the Barts Cancer Institute. With a PhD from the University of Rovira I Virgili, Spain, he has held positions as a postdoctoral researcher at Harvard University and a Research Fellow at Queen’s University Belfast. His research lies at the convergence of medical imaging and artificial intelligence, focusing on the development of secure and ethical computational tools to enhance the detection and diagnosis of cancer diseases through image analysis. A highly productive researcher, he has authored 37 peer-reviewed publications, showcasing his exceptional technical writing skills. His involvement extends to various projects funded by esteemed entities, including the Government of Spain, UK Research & Innovation, Industrial partners, and NIH. In addition to his academic pursuits, he has fostered extensive collaborations with both academic and industry partners, underscoring his capacity to connect research endeavours with practical applications. His research interests primarily centre around the application of computer vision, machine learning, and deep learning to tackle real-world challenges in medical image analysis, particularly in digital pathology and radiology.