Precision oncology requires complex biomarkers which are often based on molecular and genetic tests of tumor tissue. For many of these tests, universal implementation in clinical practice is limited. However, for virtually every cancer patient, tissue slides stained with hematoxylin and eosin (H&E) are available. Deep Learning can extract biomarkers for better treatment decisions from these images. This talk will summarize the state of the art of Deep Learning in oncology for precision oncology biomarkers. It will cover the technical foundations, emerging use cases and established applications which are available for clinical use already.
Prof. Dr. Jakob Nikolas Kather, MD, MSc
Else Kröner Professor for Clinical Artificial Intelligence
Technical University, Dresden
Professor Jakob Kather holds dual appointments in medicine and computer science at the Technical University (TU) Dresden, Germany, serves as a senior physician in medical oncology at the University Hospital Dresden and holds an additional affiliation with the National Center for Tumor Diseases (NCT) in Heidelberg. His research is focused on applying artificial intelligence in precision oncology. Prof. Kather’s research team at TU Dresden is using deep learning techniques to analyze a spectrum of clinical data, including histopathology, radiology images, textual records, and multimodal datasets. Guided by the belief that medical and tech expertise needs to be combined, medical researchers in his team learn computer programming and data analysis, while computer scientists are immersed in cancer biology and oncology. Prof. Kather chairs the “Working group on Artificial Intelligence” at the German Society of Hematology and Oncology (DGHO) and is a member of the pathology task force of the American Association for Cancer Research (AACR). His work is supported by numerous European and national grants, which enable the team to develop new deep learning methods for medical data analysis techniques and to apply them in precision oncology.
In person: TBD
Hosted by: Mahmood Lab