In small informal 5 minute presentations various tools used in day-to-day digital pathology research will be introduced. This workshop is intended for researchers in digital pathology to get to know new tools they could incorporate into their workflow. As a keynote speaker Dr. Andreas Poehlman will present Paquo a python based interface for QuPath to …

Thursday, February 3rd 2022 9:00-17:00 Zoom Link: TBA to registered participants LINK TO REGISTER: https://docs.google.com/forms/d/1ZuRDMspp73dymKRvCSNwyoPowe32g47DIM_1pGWUeqw  

Coder's Club aims to bring together all the programmers, developers, and coders in digital pathology to discuss best practices in developing digital- and computational pathology workflows and applications. Some of the leading experts in the field are invited to discuss your questions and give you new ideas and insights into how to set up your …

REGISTER here and disseminate to your institutes and interested colleagues! https://docs.google.com/forms/d/1xYUy3qHh2S1_rbZMr6437oTrLG9Ou_wnKKvXe6S58rE/edit   SDiPath is delighted to announce our next big event: “AI industry fair” taking place January 19th, 2023 from 9:30am to 5pm in Bern, in-person. We have gathered some of the big names in AI algorithm development in pathology to pitch us their products, …

Join us for our Trailblazers in AI Series! Register here: https://docs.google.com/forms/d/e/1FAIpQLSeElhR830Xyf2qrZYgHhdGtO7Yd1bRCZmmBgg2fpZdJ5xUXKA/viewform

AI in Digital Pathology: Opportunities and Challenges Abstract: The introduction of scanners that are capable of digitizing microscopic slides at high magnification has led to an explosion of interest in computational pathology in general and deep learning applied to whole slide images (WSIs) in particular. In my lab at Sunnybrook, we are developing AI models …

AI for medicine; what is the medicine for AI? Abstract: In times when AI, with its latest trends on foundation models andGenerative AI has the power to influence decision support and discovery in so many domains, the question is how it impacts the delivery and discovery in the area of Healthcare & lifescieneces. What is …

Learning from imperfect annotations in digital pathology Abstract : Many machine learning applications in the field of digital pathology rely on images annotated by human experts. However, as in other areas of medical imaging, these experts are not infallible on these often laborious tasks, and may even disagree with each other on complex tasks. These …

Abstract: Our speaker is Dr. Harshita Sharma who will present her recent work in histopathology at Microsoft, to enable large-scale screening of Barrett's esophagus using weakly supervised deep learning. She will also give an overview of open-source computational pathology tools developed by Microsoft. Bio:I am a Senior Researcher in the Biomedical Imaging team at Microsoft …

Title: Multiplexed imaging for next-generation pathology, challenges and opportunities. Abstract: Tissues are spatially organized ecosystems that are comprised of distinct cell types, each of which can assume a variety of phenotypes defined by coexpression of multiple proteins. To underscore this complexity it is essential to interrogate cellular expression patterns within their native context in the …

Please save the date and REGISTER here and disseminate to your institutes and interested colleagues! https://docs.google.com/forms/d/18wFmEx-f1DNPEpSYL1zVrn7pGJaKn309ytn37RkRtZs Please save the date for our next Annual Meetings, taking place January 25th, 2024 from 9:30am to 4:15pm in Bern, in-person. Topics will include: Digital and Computational Pathology in three educational sessions: from the data to the algorithm and …

Title: Deep learning approaches for identifying predictive biomarkers from the tumour microenvironment Abstract: Tumour microenvironment plays a critical role in cancer progression and resistance. Histopathology and cellular imaging approaches allow capturing spatial organisation of different cell types in the tumour microenvironment and their potential interactions. While convolutional neural networks demonstrated a great performance in classifying …