The SciLifeLab BioImage Informatics facility welcomes you to Uppsala University for a Workshop on Digital Pathology:
Venue: Centre for Image Analysis, Dept. IT, UU, Polacksbacken (Lägerhyddsvägen 2) House 1, room 1311 (3rd floor)
The day is free of charge, funded via SciLifeLab BioImageInformatics facility (https://scilifelab.se/facilities/bioimage-informatics/). To secure a spot, please sign up via Carolina Wählby email@example.com. Bring your own laptop for the hands-on session.
10:15 – 15:00 Introduction to QuPath
QuPath is open source software for digital pathology providing flexible, user-friendly tools for pathologists, biologists and algorithm developers working with whole slide images. This workshop offers a practical, hands-on introduction to viewing and annotating images with QuPath, before moving on to show how it can be used to rapidly detect, classify and quantify thousands of cells across large tissue images and tissue microarrays.
based on biomarker staining intensity
Instructor: Pete Bankhead developed QuPath while a
postdoc at Queen’s University Belfast. He has a background in creating and teaching open source tools for bioimage analysis, and wrote an introductory handbook for biologists, Analyzing Fluorescence Microscopy Images with ImageJ. Pete currently works as a Senior Image Analyst at Philips Digital Pathology Solutions, Belfast.
15:15-16:00 Deep learning for breast cancer histopathology
Deep learning is a state-of-the-art pattern recognition technique that has been found extremely powerful for analysis of digitized histopathological slides. In our research we study different applications of this technique for improved diagnostics and prognostics of breast cancer patients. Histopathological assessment of the axillary lymph node status is one of the three steps in breast cancer staging. Automated assessment of the lymph node status is therefore a straightforward high potential application for automated assessment. Our current algorithms for this task perform equally well as trained pathologists, making them suitable for large scale routine validation and implementation. We also developed algorithms for fully automated recognition and counting of mitotic figures, which aids breast cancer grading. As a result of these techniques, routine diagnostics becomes more efficient and reproducible. More advanced automated techniques are developed to identify novel prognostic biomarkers, contributing to the development of personalized medicine. We study the tumor to stroma ratio, the presence of tumor infiltrating lymphocytes and the appearance of the tumor stroma as possible future prognosticators.
Lecturer: Dr Jeroen van der Laak, PhD, is associate professor in computational Pathology at the Department of Pathology of the Radboud University Medical Center in Nijmegen, The Netherlands. His research focuses on the use of deep learning based analysis of whole slide images for different applications: improvement of routine pathology diagnostics, objective quantification of immunohistochemical markers, and study of novel imaging biomarkers for prognostics. Dr van der Laak has an MSc in computer science and acquired his PhD from the Radboud University in Nijmegen. He co- authored over 85 peer-reviewed publications and is member of the editorial boards of Laboratory Investigation and the Journal of Pathology Informatics. He is member of the board of directors of the Digital Pathology Association and organizer of sessions at the European Congress of Pathology and the Pathology Visions conference. He coordinated the CAMELYON grand challenges in 2016 and 2017. Dr van der Laak acquired research grants from the European Union and the Dutch Cancer Society, among others. He is frequently invited as a speaker at international conferences.
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