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

When

15th Dec 2020

Where

Online

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

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Digital image analysis using AI has great utility to standardize data, increase biomarker discovery and aid in diagnostic assessment of disease. For image analysis in clinical applications, AI development relies on indication-specific training sets to develop and train the algorithm, as well as separate images for testing. However, once the algorithm leaves the development team, there is an unavoidable element of uncertainty from the vastly greater pool of image sets that algorithm will encounter.

  • How do we know we can trust the models used to develop the APP?
  • Is there a way to test the confidence predictions coming from these models?

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.

Date: 15 December 2020
Time: New York – 11:00 | London – 16:00 | Paris – 17:00 | Singapore – 00:00 (16 December) |Tokyo – 01: 00 (16 December) |Sydney – 03:00 (16 December)
Duration: 60 minutes
Event structure: 1 presentation + 15-minute Q&A
Registration fee: Complimentary access
Webinar on-demand: Available to view until midnight 29 December 2020 (registration is required)

Jeppe specializes in advanced machine learning and medical image analysis with an emphasis on applying deep learning to various healthcare applications, especially within cancer research and neuroscience. At Visiopharm, he is the lead developer of our deep learning platform and has a strong involvement in all new algorithm developments. Jeppe is also an Industrial PhD Fellow at DTU Compute researching new methods to improve deep learning methods for pathology, and holds an M.Sc. in Biomedical Engineering from the Technical University of Denmark (DTU) and University of Copenhagen.

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For further details please contact
Gavin Hambrook
Telephone +44 (0) 7538 368 764 or
email [email protected]

Reuben Raj
Direct: +603 2117 5193
email [email protected]

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