{"id":735667,"date":"2021-01-28T07:28:45","date_gmt":"2021-01-28T15:28:45","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=735667"},"modified":"2021-03-23T07:41:39","modified_gmt":"2021-03-23T14:41:39","slug":"project-innereye-augmenting-cancer-radiotherapy-workflows-with-deep-learning-and-open-source","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/project-innereye-augmenting-cancer-radiotherapy-workflows-with-deep-learning-and-open-source\/","title":{"rendered":"Project InnerEye: Augmenting cancer radiotherapy workflows with deep learning and open source"},"content":{"rendered":"

Medical images offer vast opportunities to improve clinical workflows and outcomes. Specifically, in the context of cancer radiotherapy, clinicians need to go through computer tomography (CT) scans and manually segment (contour) anatomical structures. This is an extremely time-consuming task that puts a large burden on care providers. Deep learning (DL) models can help with these segmentation tasks. However, more understanding is needed regarding these models\u2019 clinical utility, generalizability, and safety in existing workflows. Building these models also requires techniques that are not easily accessible to researchers and care providers.<\/p>\n

In this webinar, Dr. Ozan Oktay and Dr. Anton Schwaighofer will analyze these challenges within the context of image-guided radiotherapy procedures and will present the latest research outputs of\u202fProject InnerEye\u202fin tackling these challenges. The first part of the webinar will focus on a research study that evaluates the potential clinical impact of DL models within the context of radiotherapy planning procedures. The discussion will also include the performance analysis of state-of-the-art DL models on datasets from different hospitals and cancer types, and we\u2019ll explore how they compare with manual contours annotated by three clinical experts.<\/p>\n

The second part of the talk will introduce the open-source InnerEye Deep Learning Toolkit and how it can provide tools to help enable users to build state-of-the-art medical image segmentation models in Microsoft Azure. There will be examples illustrating step-by-step how the toolkit can be used in different segmentation applications within Azure Machine Learning (Azure ML) infrastructure. This includes model specification, training run analysis, performance reporting, and model comparison.<\/p>\n

In this webinar, we will explore:<\/p>\n