The today and tomorrow of image analysis in digital pathology. Roundtable discussion
- The evolution and adoption of digital pathology
- H&E vs. immunostainings
- Fluorescent vs. chromogenic – upsides and downsides for image analysis
- Practical considerations for high-dimensional analysis
Dr. Inti Zlobec – Head of the Translational Research Unit (TRU), University of Bern, Switzerland
Inti Zlobec received her PhD in Experimental Medicine (Pathology) from McGill University in Montreal. By combining her interests in pathology and biostatistics, she has developed biostatistical models for prediction of response to preoperative radiotherapy in patients with rectal cancer and optimized workflows for biomarker analysis. In 2011, she received the title of ‘Privatdozentin’ from the University of Basel. Today, Inti Zlobec works as Associate Professor at the University of Bern, where she leads the Translational Research Unit and Tissue Bank Bern. She is Chair of the Working Group IT for the European Society of Pathology and President of the Swiss Consortium for Digital Pathology (SDiPath). Her research group includes a multi-disciplinary team using experimental methods and digital pathology to gain insights into the biology of colorectal cancers, their molecular subtypes, prognoses and therapy responsiveness with the aim of improving patient management.
Dr. Andrew Janowczyk – Assistant Professor, Department of Biomedical Engineering, Case School of Engineering, Switzerland
Dr Andrew Janowczyk is an Assistant Research Professor at The Center of Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University, and a Senior Research Scientist in the Precision Oncology Center at the Lausanne University Hospital (CHUV), Switzerland. For over 10 years he has applied computer vision algorithms to digital pathology images. One of his areas of expertise is in leveraging deep learning to build computational models to aid pathologists in many common tasks, such as disease detection and grading. Dr Janowczyk’s 2016 Journal of Pathology Informatics Paper entitled “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases” (over 30k views), laid out a generalized approach via open-sourced code and datasets to facilitate the development of the next generation of data scientists. In 2018, his tool “HistoQC: A quality control pipeline for digital pathology slides” received the Innovation Award at the European Congress of Digital Pathology (ECDP). He helped co-found and was elected secretary of the Swiss Digital Pathology Consortium (SDiPath). His newer research focuses on the tasks of predicting prognosis and therapy response. He maintains a research-oriented blog, andrewjanowczyk.com, which aims to provide digital pathology related insights, code, and datasets to the research community.