Recently, Professor Yin Ming from the School of Electronic Science and Engineering (School of Microelectronics), Faculty of Engineering, South China Normal University, has achieved a significant accomplishment in the field of medical imaging and machine learning. The paper titled "Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review," with Professor Yin Ming as the corresponding author, has been honored with the First Prize of the 2025 IEEE Engineering in Medicine and Biology Society (EMBS) Paper Award. Additionally, the paper was recognized as the "Best Paper of the Year" by the journal IEEE Reviews in Biomedical Engineering, fully demonstrating the team's innovative achievements and academic influence in the relevant field.

    IEEE Reviews in Biomedical Engineering is a highly regarded journal in the field of biomedical engineering. The journal's impact factors for 2023–2025 are 17.6, 17.2, and 12.0, respectively, and it is rated as a Tier 1 journal in the Engineering and Technology category according to the Chinese Academy of Sciences (CAS) classification.

   The research was jointly completed by the School of Electronic Science and Engineering (School of Microelectronics), Faculty of Engineering, South China Normal University, and the Guangdong Provincial Key Laboratory of Chip and Integration Technology. The study explores machine learning-based image segmentation techniques for MRI brain tumor detection, driving advancements in medical image analysis and intelligent diagnostic technologies, and holds significant academic value and application potential.

    This honor received by Professor Yin Ming's team not only highlights the outstanding performance of South China Normal University on the international academic stage but also demonstrates the university's leading position in the fields of integrated technology and biomedical engineering. The research findings provide new insights for the field of medical image analysis and offer valuable reference for the future development of tumor detection and diagnostic technologies.