{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/d216e525-3568-4f94-b402-8a3eaa1ad260/62038f3ac36dc300125e7c26?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Machine Learning facilitates assessment of shaved lumpectomy margins ","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/61b9f39f1a8cbe7bbf3cedc6/show-cover.png?height=200","description":"<p>Drs. Timothy D’Alfonso, David Ho, and Lee K.Tan from Memorial Sloan Kettering in NY discuss their&nbsp;recent Modern Pathology study describing a new machine learning algorithm that can help pathologists assess shaved margins from lumpectomy specimens. A uniquely developed Deep Multi-Magnification Network (DMMN) was utilized in Whole Slide Images (WSI) in the hope to triage negative margins and allow pathologists to focus on shaves that are positive for DCIS and/or Invasive carcinoma.</p>","author_name":"Modern Pathology"}