Browsing by Author "Kalpathy-Cramer, Jayashree"

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    A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges
    (American Association for Cancer Research, 2024) Gomez, Felicia; Danos, Arpad M.; Del Fiol, Guilherme; Madabhushi, Anant; Tiwari, Pallavi; McMichael, Joshua F.; Bakas, Spyridon; Bian, Jiang; Davatzikos, Christos; Fertig, Elana J.; Kalpathy-Cramer, Jayashree; Kenney, Johanna; Savova, Guergana K.; Yetisgen, Meliha; Van Allen, Eliezer M.; Warner, Jeremy L.; Prior, Fred; Griffith, Malachi; Griffith, Obi L.; Pathology and Laboratory Medicine, School of Medicine
    People diagnosed with cancer and their formal and informal caregivers are increasingly faced with a deluge of complex information, thanks to rapid advancements in the type and volume of diagnostic, prognostic, and treatment data. This commentary discusses the opportunities and challenges that the society faces as we integrate large volumes of data into regular cancer care.
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    Privacy preservation for federated learning in health care
    (Elsevier, 2024-07-12) Pati, Sarthak; Kumar, Sourav; Varma, Amokh; Edwards, Brandon; Lu, Charles; Qu, Liangqiong; Wang, Justin J.; Lakshminarayanan, Anantharaman; Wang, Shih-han; Sheller, Micah J.; Chang, Ken; Singh, Praveer; Rubin, Daniel L.; Kalpathy-Cramer, Jayashree; Bakas, Spyridon; Pathology and Laboratory Medicine, School of Medicine
    Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.