Rate My Professor Jinshan Tang

Dr. Tang's research focuses on medical image analysis, artificial intelligence in healthcare, biomedical imaging, computer-aided cancer detection, AI for COVID-19 detection and diagnosis, big data analysis, and bioinformatics. He has authored over 130 refereed journal and conference papers, with his work cited more than 6,500 times. Key publications include “Entropy-Guided Dynamic Competition Framework for Semi-Supervised Medical Image Segmentation” (Applied Soft Computing, 2026), “Weakly supervised segmentation of retinal layers on OCT images with AMD using uncertainty prototype and boundary regression” (Medical Image Analysis, 2025), “Attention-based Cross-Domain Synthesis and Segmentation from Unpaired Medical Images” (IEEE Transactions on Emerging Topics in Computational Intelligence, 2024), “An Attention Residual U-Net with Differential Preprocessing and Geometric Postprocessing: Learning How to Segment Intracranial Aneurysms” (Medical Image Analysis, 2023), “Weakly Supervised Segmentation of COVID-19 Infection with Scribble Annotation on CT Images” (Pattern Recognition, 2022), and “Surface Extraction and Thickness Measurement of the Articular Cartilage from MR Images using Directional Gradient Vector Flow Snake” (IEEE Transactions on Biomedical Engineering, 2006). His achievements include the Best Paper Award at the SPIE Conference on Mobile Multimedia/Image Processing, Security, and Applications (2015), Achievement Award at the 2001 International Multi-conference, top 10% teacher recognition at Michigan Technological University (Spring 2014), Senior Member of IEEE (2003~), co-chair of the IEEE SMC Technical Committee on Information Assurance and Intelligent Multimedia-Mobile Communications (2016~), multiple international keynote speeches, and over three million dollars in grants as PI or co-PI.