Orcun Göksel is a Professor in the Department of Information Technology at Uppsala University's Faculty of Science and Technology, specializing in medical imaging and computational analysis. His research group Vi3; Image Analysis focuses on developing novel imaging techniques with applications in oncology, dermatology, and biomechanics. His primary research interests span medical imaging physics , particularly speed-of-sound reconstruction for breast density assessment and cancer diagnostics, deep learning applications in digital pathology including whole-slide image analysis, and computational biomechanics for orthopedic applications. His work bridges ultrasound physics, machine learning, and clinical medicine to develop quantitative imaging biomarkers. Analysis of his recent publications (2021-2025) reveals three dominant research thrusts: (1) Speed-of-sound imaging as a novel quantitative biomarker using conventional ultrasound hardware, (2) Domain adaptation techniques for medical image segmentation across heterogeneous datasets, and (3) Computational pathology methods for cancer diagnosis from whole-slide images. His group has published extensively in Medical Image Analysis , IEEE Transactions on Medical Imaging , and European Radiology . Göksel maintains active collaborations with clinical departments including radiology (breast imaging) and pathology (prostate cancer diagnostics), as evidenced by co-authorship with medical specialists. His laboratory develops both hardware-accelerated imaging techniques and deep learning frameworks for medical image analysis, with strong emphasis on interpretable and robust AI solutions for clinical deployment.
