Deep learning from multiple experts improves identification of amyloid neuropathologies

Deep learning from multiple experts improves identification of amyloid neuropathologies

Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address this problem, we present a deep learning (DL) approach that draws on a cohort of experts, weighs each contribution, and is robust to noisy labels. We c...

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Bibliographic Details
Main Authors: Athey, J. (Author), Borys, E. (Author), Butte, A.J (Author), Das, S. (Author), Dugger, B.N (Author), Flanagan, M.E (Author), Keiser, M.J (Author), Kofler, J.K (Author), McAleese, K.E (Author), Mew, N.C (Author), Tang, Z. (Author), White, C.L., III (Author), Wong, D.R (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
Algorithms
Amyloid beta
Consensus
Deep learning
Expert annotators
Histopathology
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