Webinars

Check out our featured webinars about digital pathology, deep learning and our latest breakthroughs

Artificial intelligence in digital pathology

Artificial intelligence in digital pathology

Branden Hopkinson
Jr. Product Marketing Manager

Join Branden Hopkinson, who will talk about using AI classifiers to find patterns in your images and which artificial intelligence method will work best with your image data. The terms “artificial intelligence” (AI), “machine learning” (ML), and “deep learning” (DL) have infiltrated nearly every industry. This presentation will define the important differences between each term, provide understanding of their roles in image analysis, and help you to decide which questions to ask and when to employ each method in your research.

Presented at the LabRoots virtual Cancer Research & Oncology 2020 event on October 7, 2020.

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Predictive uncertainty: Knowing what we don’t know in image data

Predictive uncertainty: Knowing what we don’t know in image data

Jeppe Thagaard
Computer Vision & AI Engineer, Team Lead

In this talk, we will provide insights into how we work with concepts of uncertainty and how these affect our development of APPs and datasets for assisting primary diagnosis. We will also give examples from our recent research, development and validation of our lymph node metastasis detection AI APP.

Presented at the Global Engage’s virtual 7th Digital Pathology & AI Congress: EU on December 3, 2020 and during an exclusive webinar with Global Engage on December 15, 2020.

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The inhibitory role of specific macrophages in RCC

The inhibitory role of specific macrophages in RCC

Dr. Elfriede Nößner
Head of Immunoanalytics, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany

In this webinar, Immunologist Prof. Dr. Elfriede Nößner will present her research on specific macrophages in RCC (“ercDCs”) which are associated with poor outcome and their potential as new targets in immune therapy.

Presented on Tuesday, January 19, 2021

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Investigating tumor blood vessels via digital pathology: spotlighting on breast cancer and glioblastoma

Investigating tumor blood vessels via digital pathology: spotlighting on breast cancer and glioblastoma

Dr. Giorgio Seano
Head of the Tumor Microenvironment Lab, Institut Curie, Orsay-Paris (France)

Dr. Seano will present an overview of his works on blood vessel histological investigation. The study of the abnormalities of tumor blood vessels elucidated tumor features and is still shedding light on its pathology. Tumor vessels may be characterized by abnormal morphology, disrupted vascular basement membrane and reduced pericyte coverage. This causes dysfunction in perfusion and consequently leads to an hypoxic and immunosuppressive environment, typical of tumors. Breast cancer and glioblastoma are two of the most important tumors in the field of vascular microenvironment since we learnt that we can therapeutically modulate and temporally normalize vascular function. Dr. Seano will show digital pathology results published during his period at Harvard Medical School and unpublished data from his own new lab.

Presented at the Pathology Visions 2020 virtual meeting during Visiopharm’s Industry Workshop on Monday, October 26, 2020.

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Fluorescence analysis – using deep learning to stratify mitotic events in complex cellular data

Fluorescence analysis – using deep learning to stratify mitotic events in complex cellular data

Joseph R. Daniele, PhD
Institute Research Scientist

Are you getting the maximum out of your sample images?

In this webinar, Joseph Daniele Ph.D. will demonstrate the power of deep learning to investigate and quantify variability within a cell.

Determining mitotic activity is a common component of many tumor grading systems but relies on tedious identification and enumeration of mitotic figures within selected fields of view. Using Visiopharm’s deep learning module, Joe and his group have trained a classifier to automatically identify and count a range of mitotic figure morphologies in an entire tissue sample.

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Extraction of histomorphological data using artificial intelligence

Extraction of histomorphological data using artificial intelligence

Ralf Huss, MD, PhD
Professor of Pathology, University Hospital in Augsburg (Germany)

Data extraction data from virtual slides is the foundation for the identification of novel patterns that are difficult or impossible to quantify by the human eye. Along with innovative technologies like high-plex immunofluorescence, artificial intelligence allows the caption of deeper insights, contextual information and describe spatial relationships.

Presented at the Pathology Visions 2020 virtual meeting during Visiopharm’s Industry Workshop on Monday, October 26, 2020.

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