Federated learning for computational pathology on gigapixel whole slide images

Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from histology such as molecular alterations. However, the...

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Bibliographic Details
Main Authors:	Chen, R.J (Author), Chen, T.Y (Author), Kong, D. (Author), Lipkova, J. (Author), Lu, M.Y (Author), Mahmood, F. (Author), Singh, R. (Author), Williamson, D.F.K (Author)
Format:	Article
Language:	English
Published:	Elsevier B.V. 2022
Subjects:	adult article attention Computational pathology controlled study Curation Data Sharing deep learning Deep learning Diagnosis Differential privacies Excel Federated learning female histology Histology histopathology human human tissue Large dataset Learning Based Models male noise Pathology Population statistics privacy Privacy-preserving techniques randomized controlled trial software Split learning survival prediction Whole slide images Whole slide imaging
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Federated learning for computational pathology on gigapixel whole slide images

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