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

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 confidence i...

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Veröffentlicht in:	BMC medical imaging Jg. 23; H. 1; S. 1 - 9
Hauptverfasser:	Gudhe, Naga Raju, Kosma, Veli-Matti, Behravan, Hamid, Mannermaa, Arto
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 19.10.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Algorithms Approximation Artificial neural networks Bayesian analysis Bayesian deep learning Cancer Datasets Deep learning Diagnostic systems Digital pathology Epistemology Histochemistry Histology Histopathology Image analysis Image processing Image segmentation Imaging Instance segmentation Machine learning Mathematical models Medical image analysis Medical imaging Medical imaging equipment Medicine Medicine & Public Health Model accuracy Monte Carlo simulation Neural networks Nuclei Nuclei segmentation Performance evaluation Predictions Radiology Representations Semantic segmentation Survival analysis Uncertainty Uncertainty estimation Visual fields Digital pathology Bayesian deep learning Uncertainty estimation Nuclei segmentation Semantic segmentation Medical image analysis
ISSN:	1471-2342, 1471-2342
Online-Zugang:	Volltext
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