• Dr. Aristotelis Tsirigos on Pitt HexAI

  • 2024/07/21
  • 再生時間： 34 分
  • ポッドキャスト

Dr. Aristotelis Tsirigos on Pitt HexAI

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  • Jordan Gass-Poore’, Pitt HexAI host, delves into the cutting-edge world of bioinformatics and cancer research with Dr. Aristotelis Tsirigos, a Professor of Medicine and Pathology and the Co-Director of Precision Medicine at NYU Grossman School of Medicine. Dr. Tsirigos shares his journey from computer science and electrical engineering to becoming a leading figure in pathology and bioinformatics.


    In this episode, they explore the latest advancements in self-supervised deep learning and its transformative impact on cancer diagnosis and management. Dr. Tsirigos discusses his former work at IBM and his current research at NYU Langone Health's Perlmutter Cancer Center, including the innovative study "Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides." This study, conducted in collaboration with the University of Glasgow, leverages AI to identify distinct cancer phenotypes, offering new insights into cancer subtypes and potential therapeutic strategies.


    Tune in to learn about the intersection of AI, molecular information, and healthcare, and how these advancements are paving the way for more precise and personalized cancer treatments
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あらすじ・解説

Jordan Gass-Poore’, Pitt HexAI host, delves into the cutting-edge world of bioinformatics and cancer research with Dr. Aristotelis Tsirigos, a Professor of Medicine and Pathology and the Co-Director of Precision Medicine at NYU Grossman School of Medicine. Dr. Tsirigos shares his journey from computer science and electrical engineering to becoming a leading figure in pathology and bioinformatics.


In this episode, they explore the latest advancements in self-supervised deep learning and its transformative impact on cancer diagnosis and management. Dr. Tsirigos discusses his former work at IBM and his current research at NYU Langone Health's Perlmutter Cancer Center, including the innovative study "Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides." This study, conducted in collaboration with the University of Glasgow, leverages AI to identify distinct cancer phenotypes, offering new insights into cancer subtypes and potential therapeutic strategies.


Tune in to learn about the intersection of AI, molecular information, and healthcare, and how these advancements are paving the way for more precise and personalized cancer treatments
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