معرفی
Anders Tingberg serves as Associate Professor in Medical Radiation Physics at Lund University, with dual appointments at both the Malmö and Lund campuses. He is a Principal Investigator at the Lund University Cancer Centre (LUCC) and holds an adjunct professorship in the same department. His work spans medical physics, cancer imaging, and radiation dosimetry with strong ties to clinical applications.
His research focuses on advanced medical imaging techniques, particularly digital mammography and breast tomosynthesis. Key interests include image quality optimization, radiation dose reduction, AI integration in diagnostic workflows, and simulation of breast tissue heterogeneity. His fingerprint analysis reveals dominant expertise in Images Engineering (100%), Digital Mammography (53%), and Breast Tomosynthesis (53%), with significant contributions to lesion analysis and diagnostic accuracy improvement.
Recent publications demonstrate a clear trend toward AI-enhanced imaging solutions and hybrid imaging techniques. His 2024-2025 work explores noise reduction algorithms in pelvic radiography, simultaneous breast tomosynthesis with mechanical imaging, and AI-based lesion risk scoring across exposure parameters. These studies consistently address the critical balance between diagnostic image quality and radiation safety.
- 1st Prize Live Demonstrations Workshop (2025)
- 1st Prize Live Demonstrations Workshop (2023)
Tingberg actively supervises doctoral students and leads multiple research projects focused on breast cancer imaging and spectral CT. His grants include funding from UMAS Cancerstiftelse and Bröstcancerförbundet. Current work involves AI integration in mammography (2020-present) and spectral CT brain imaging development (2021-2025). He also organizes collaborative events like the LUCC-MCC Seminar on Computational Simulation Studies in Cancer Imaging.
As a core member of Lund University's Breast Cancer Imaging network and the DBTMI research group, Tingberg collaborates extensively with international researchers including P. Bakic, S. Zackrisson, and M. Dustler. His lab specializes in computer simulations of image acquisition, glandular dose modeling, and AI-driven diagnostic tools for oncology applications.
