Marco Gustav

Deep Learning-based Biomarkers for Precision Oncology

Abstract

The use of deep learning (DL) in oncology has opened a transformative era for precision medicine. This talk will focus on the development and validation of DL-based biomarkers that have the potential to advance cancer diagnosis, prognosis, and treatment. By utilizing the vast amounts of healthcare data generated by modern technologies, DL algorithms can identify complex patterns – even those, which elude human detection.

These biomarkers are important for understanding tumor biology and patient heterogeneity, which can lead to more personalized and effective therapeutic strategies. We examine recent developments in deep learning techniques, the challenges of integrating artificial intelligence into clinical workflows, and the implications of algorithmic decision making for patient care. 

Biography/Link

Marco Gustav earned his Bachelor’s and Master’s degrees in Mechanical Engineering from Leibniz University Hannover (LUH), Germany. He then worked as a Research Associate in Biomaterials and Biomechanics at Hannover Medical School (MHH), where he focused on Orthopedic Implant Research and Deep Learning for Gait Analysis.

Marco Gustav is currently pursuing his PhD in Clinical Artificial Intelligence under the supervision of Prof. Jakob N. Kather at the Else Kröner Fresenius Center for Digital Health, Dresden University of Technology (TUD), Germany. His research focuses on AI in medicine, specifically Computational Pathology and Large Language Models. He is passionate about making AI in medicine more accessible and understandable for everyone, which he translates into Science Slams.