Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning

Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning

Abstract Background The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model prediction accuracy without assessing the con...

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Detalhes bibliográficos
Main Authors: Naga Raju Gudhe, Veli-Matti Kosma, Hamid Behravan, Arto Mannermaa
Formato: Artigo
Idioma:English
Publicado em: BMC 2023-10-01
Colecção:BMC Medical Imaging
Assuntos:
Semantic segmentation
Bayesian deep learning
Uncertainty estimation
Nuclei segmentation
Digital pathology
Medical image analysis
Acesso em linha:https://doi.org/10.1186/s12880-023-01121-3

Internet

https://doi.org/10.1186/s12880-023-01121-3

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