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Made Possible By:
Radboud University Medical Center Computational Pathology Group
Radboud university medical center is a leading academic centre for medical science, education, and health care in Nijmegen, The netherlands. The computational pathology group develops, validates and deploys novel medical image analysis methods based on deep learning technology and focusing on computer-aided diagnosis.
Charles River Laboratories
Charles River provides products and services to help expedite the discovery, early-stage development and safe manufacture of novel drugs and therapeutics. Charles River's Digital Pathology offering supports primary and peer review, telepathology, image analysis, and stereology applications. It is the first non-clinical pathology group in the world with a fully GLP validated digital microscope for completing primary and peer-review pathology evaluations for toxicology studies.
Global Engage Ltd.
Global Engage is on the mission to be the leading interface connecting business and the life sciences; by delivering excellence through the creation of world-class events and content across the globe.
Aleksandra Zuraw, DVM, PhD
Aleksandra is a veterinary pathologist at Charles River Laboratories and a digital pathology publisher at The Digital Pathology Place. Her mission is to bridge the gap between pathology and computer sciente to advance digital pathology as a tool to help more patients and develop better drugs faster. This event is a result of her sabbtical program at Charles River Laboratories.
LIVE With Digital Pathology Bridge Builders
November 4th @ 7:00 AM EST
Presented by Aleksandra Zuraw
ONLY LIMITED SEATS AVAILABLE!
Meet the Speakers
JEROEN VAN DER LAAK
Professor of computational Pathology at the Department of Pathology of the Radboud University Medical Center in Nijmegen & guest professor at the Center for Medical Image Science and Visualization (CMIV) in Linkoping, Sweden.
RESEARCH FOCUS:
Improving cancer diagnostics and prognostics with machine learning and large data sets in Pathology
GEERT LITJENS
Assistant Professor at Radboud University Nijmegen Medical Center
RESEARCH FOCUS:
Application of modern machine learning methods to oncological pathology (focus on prostate and pancreatic cancer)
FRANCESCO CIOMPI
Assistant Professor of Computational Pathology at Radboud University Medical Center, Nijmegen.
RESEARCH FOCUS:
AI in precision oncology, computer-aided diagnosis for large-scale digital pathology and multi-modal data.
DAAN GEIJS
PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center
RESEARCH FOCUS:
Implementing deep learning in the daily routine of dermatopathologists
LESLIE TESSIER
PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center & Resident Physician (Pathology), CHU Angers, France
RESEARCH FOCUS:
Automated assessment of tubule formation in breast cancer
LEANDER VAN EEKELEN
PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center.
RESEARCH FOCUS:
Improving lung cancer immunotherapy with deep learning
MEYKE HERMSEN
Study manager and PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center.
RESEARCH FOCUS:
Deep learning applications for renal transplant pathology
KHRYSTYNA FARYNA
PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center.
RESEARCH FOCUS:
Bridging the clinical integration gap for deep learning-based methods in computational pathology by improving model generalization
EDUARD CHELEBIAN
PhD candidate in the Department of Information Technology at Uppsala University, Sweden.
RESEARCH FOCUS:
What representations do deep neural networks learn in histopathology imaging and their connection to spatially-resolved gene expression
MILDA POCEVICIUTE
PHD Candidate in the Computer Graphics and Image processing Group at Linköping University, Sweden.
RESEARCH FOCUS:
Explainable artificial intelligence (XAI), anomaly detection and uncertainty techniques for digital pathology
What You'll Learn On This FREE Event!
LEARN #1:
"What were the beginnings of computational pathology? How far along are we now and where are we heading? What do we need to improve in the current setup to work better and faster?"
LEARN #2:
"Which image analysis approach to choose for your pathology images? Should if be classical computer vision or deep learning based approach? Object detection or semantic segmentation? After this event you will know!"
LEARN #3:
"What other type of data in addition to images can we leverage in computational pathology? How can we integrate them in the research pipeline and what does it mean in clinical pathology?"
Yes, I want to help bridge the gap between PATHOLOGY and COMPUTER SCIENCE. Count me in!
By providing your email address you are allowing Digital Pathology Place, Inc. and Global Engage Ltd. to send you email messages. Don't worry we will only send you relevant information and if you don't like it you can unsubscribe any time.
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