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. 2024 May;6(3):e240126.
doi: 10.1148/ryai.240126.

When the Student Becomes the Master: Boosting Intracranial Hemorrhage Detection Generalizability with Teacher-Student Learning

Nathaniel Swinburne  1
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When the Student Becomes the Master: Boosting Intracranial Hemorrhage Detection Generalizability with Teacher-Student Learning

Nathaniel Swinburne. Radiol Artif Intell. 2024 May.
No abstract available

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Conflict of interest statement

Disclosures of conflicts of interest: N.S. Research grant from the Innovation in Cancer Informatics (ICI) program; patent issued to Memorial Sloan Kettering Cancer Center related to the use of semisupervised learning for radiology computer vision.

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Nathaniel Swinburne, MD, is a neuroradiologist and the director of radiology informatics at Memorial Sloan Kettering Cancer Center in New York City. Leading a team of informaticists, he focuses on leveraging technology to increase workflow efficiency and advance clinical artificial intelligence (AI). His translational research emphasizes the use of data mining coupled with semisupervised learning techniques to accelerate the development of radiology AI models and support continuous model learning.
Nathaniel Swinburne, MD, is a neuroradiologist and the director of radiology informatics at Memorial Sloan Kettering Cancer Center in New York City. Leading a team of informaticists, he focuses on leveraging technology to increase workflow efficiency and advance clinical artificial intelligence (AI). His translational research emphasizes the use of data mining coupled with semisupervised learning techniques to accelerate the development of radiology AI models and support continuous model learning.
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References

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    1. Lin E , Yuh EL . Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation . Radiol Artif Intell 2024. ; 6 ( 3 ): e230077 . - PMC - PubMed

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