About Min Xu
Dr. Min Xu is an Affiliated Assistant Professor in Computer Vision Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
Dr. Min Xu is an Assistant Professor at the Computational Biology Department within the School of Computer Science at Carnegie Mellon University, USA. His career has centered on developing AI methods for the analysis of biomedical images and other biomedical data, in particular, Cellular Cryo-Electron Tomography (Cryo-ET) 3D image data.
Dr. Xu has published over 70 research papers in prestigious peer-reviewed conferences and journals, such as CVPR, ICCV, ISMB, MICCAI, PNAS, Bioinformatics, PLOS Computational Biology, Structure, and JSB.
Dr. Xu received a B.E. in Computer Science from the Beihang University, M.Sc from the School of Computing at the National University of Singapore, M.A. in Applied Mathematics from the University of Southern California (USC), and Ph.D. in Computational Biology and Bioinformatics from USC. He was a postdoctoral researcher at USC.
|Affiliated Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence ‐ Department of Computer Vision
Honors and Awards
- Recipient of USA NIH and NSF awards
Conference Proceeding (1)
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC) (2022)
Nuclei segmentation and classification is a crucial step utilized throughout various microscopy medical analysis applications. However, it has several challenges such as the segmentation of small objects, highly imbalanced data, and subtle differences between types ...
Journal Articles (3)
Deep-precognitive diagnosis: preventing future pandemics by novel disease detection With biologically-inspired conv-fuzzy network IEEE Access (2022)
Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel ...
Deep learning predicts EBV status in gastric cancer based on spatial patterns of lymphocyte infiltration Cancers (2021)
EBV infection occurs in around 10% of gastric cancer cases and represents a distinct subtype, characterized by a unique mutation profile, hypermethylation, and overexpression of PD-L1. Moreover, EBV positive gastric cancer tends to have higher ...
ULNet for the detection of coronavirus (COVID-19) from chest X-ray images Elsevier Ltd (2021)
Novel coronavirus disease 2019 (COVID-19) is an infectious disease that spreads very rapidly and threatens the health of billions of people worldwide. With the number of cases increasing rapidly, most countries are facing the problem ...