Orcun Göksel

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.