Lei Tong

Hello! My name is Lei Tong (Chinese: 童磊), and I am a Research Fellow at the Centre for Artificial Intelligence (CAI) in Data Science & Artificial Intelligence at AstraZeneca (UK), working with Dr. Chen Jin and Dr. Philip Teare. My research focuses on Large Multimodal Generative Models, Deep Causal Learning, and Biomedical Image Analysis.

I completed my PhD under the guidance of Prof. Huiyu Zhou at the School of Computing and Mathematical Sciences, University of Leicester. Additionally, I collaborate with Dr. Yinhai Wang and Dr. Adam Corrigan from Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca. My previous research involved Deep Learning for Unbiased Representation in Cellular Microscopy Imaging.

CV / Google Scholar / Linkedin / Github

Email: lei.tongml01@gmail.com

News

Mar 2024: Our paper CLANet has been accepted for publication in Medical Image Analysis. For more details, please read the paper.
Jun 2023: Our latest research work "CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images" is available on arxiv and the code and the dataset are accessible on github.
Jun 2022: It is a great honor for me to present our work "Transforming cell line authentication with a novel image-based AI method" on the seminar "Science for the Curious" in AstraZeneca.
Jun 2022: Two-weeks internship in Astrazenca and work with UKCCB group.
May 2022: 1 paper has been accepted by Scientific Reports.
Apr 2022: Our work "Depression detection on Twitter" is described in the news: Independent, Yahoo, Metro, .
Mar 2022: 1 paper has been accepted by IEEE trans. on Affective Computing and 1 co-authored paper has appeared on IEEE Trans. on Neural Networks and Learning Systems.
Jan 2022: We have finished the extensive revision on "Cost-sensitive Boosting Pruning Trees for depression detection on Twitter" and opensouced the github repo.
Jun 2021: Two co-authored paper has been accepted by IEEE Trans. on Medical Image and Learning Systems and Science of The Total Environment respectively.

Research

My research is focusing on computer vision and machine learning, especially in biomedical image analysis, domain generalization and ensemble learning.

	CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images Lei Tong, Adam Corrigan, Navin Rathna Kumar, Kerry Hallbrook, Jonathan Orme, Yinhai Wang, Huiyu Zhou Medical Image Analysis, 2024 code and dataset In this research, we introduce an innovative approach aimed at minimizing bias across varying bio-batch images by identifying and addressing batch effects in three distinct categories. The outcomes of our study are particularly noteworthy, as they demonstrate that our method diverges from traditional batch correction techniques. Notably, our strategy maintains the discriminative information inherent in bio-batches, while simultaneously improving the distinguishability between different cell lines.
	An automated cell line authentication method for AstraZeneca global cell bank using deep neural networks on brightfield images Lei Tong, Adam Corrigan, Navin Rathna Kumar, Kerry Hallbrook, Jonathan Orme, Yinhai Wang, Huiyu Zhou Scientific Reports, 2022 code In this work, we tansform AI techniques to authentication cell lines. The results can prove our AI method has potentials to complement STR profiling.
	Detecting and Tracking of Multiple Mice Using Part Proposal Networks Zheheng Jiang, Zhihua Liu, Long Chen, Lei Tong, Xiangrong Zhang, Xiangyuan Lan, Danny Crookes, Ming-Hsuan Yang, Huiyu Zhou IEEE Trans. on Neural Networks and Learning Systems, 2022 code / arXiv A novel method to continuously track several mice and individual parts without requiring any specific tagging.
	Cost-sensitive Boosting Pruning Trees for depression detection on Twitter Lei Tong, Zhihua Liu, Zheheng Jiang, Feixiang Zhou, Long Chen, Jialin Lyu, Xiangrong Zhang, Qianni Zhang, Abdul Sadka, Yinhai Wang, Ling Li, Huiyu Zhou IEEE Trans. on Affective Computing, 2022 code / arXiv A novel classifier, namely, Cost-sensitive Boosting Pruning Trees (CBPT), which demonstrates a strong classification ability on two publicly accessible Twitter depression detection datasets..
	Towards advancing the earthquake forecasting by machine learning of satellite data Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huiyu Zhou Science of The Total Environment, 2021 code An AdaBoost-based ensemble framework is proposed to forecast earthquake. Our results support a Lithosphere-Atmosphere-Ionosphere Coupling during earthquakes.
	CANet: Context Aware Network for Brain Glioma Segmentation Zhihua Liu, Lei Tong, Long Chen, Feixiang Zhou, Zheheng Jiang, Qianni Zhang, Yinhai Wang, Caifeng Shan, Ling Li, Huiyu Zhou IEEE Trans. on Medical Imaging, 2021 code / arXiv A novel approach named Context-Aware Network (CANet) for brain glioma segmentation.
	Structured Context Enhancement Network for Mouse Pose Estimation Feixiang Zhou, Zheheng Jiang, Zhihua Liu, Fang Chen, Long Chen, Lei Tong, Zhile Yang, Haikuan Wang, Minrui Fei, Ling Li, Huiyu Zhou IEEE Trans. on Circuits and Systems for Video Technology, 2021 code / arXiv A novel Hourglass network based model, namely Graphical Model based Structured Context Enhancement Network (GMSCENet), quantifies mouse pose estimation from videos.
	Perceptual underwater image enhancement with deep learning and physical priors Long Chen, Zheheng Jiang, Lei Tong, Zhihua Liu, Aite Zhao, Qianni Zhang, Junyu Dong, Huiyu Zhou IEEE Trans. on Circuits and Systems for Video Technology, 2020 code / arXiv In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor. The detection perceptor provides coherent information in the form of gradients to the enhancement model, guiding the enhancement model to generate patch level visually pleasing images or detection favourable images.
	Deep Learning Based Brain Tumor Segmentation: A Survey Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou arXiv, 2020 code / arXiv Considering stateof-the-art technologies and their performance, the purpose of this paper is to provide a comprehensive survey of recently developed deep learning based brain tumor segmentation techniques.
	Underwater object detection using Invert Multi-Class Adaboost with deep learning Long Chen, Zhihua Liu, Lei Tong, Zheheng Jiang, Shengke Wang, Junyu Dong, Huiyu Zhou International Joint Conference on Neural Networks (IJCNN), 2020 code / arXiv Sample-WeIghted hyPEr Network (SWIPENet) for underwater small object detection.

Teaching Assistant

2020-2023
CO7214 Service-Oriented Architecture
CO7095 Software Measurement and Quality Assurance
CO4105 Advanced C++ Programming
CO3002 Analysis and Design of Algorithms
CO2102 Database and Domain Modelling
CO1105 Introduction to Object Oriented Programming