AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

Zakaria Senousy; Mohamed Medhat Gaber; Mohammed M. Abdelsamea

doi:10.1016/j.asoc.2023.110666

AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

Zakaria Senousy, Mohamed Medhat Gaber, Mohammed M. Abdelsamea^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (SciVal)

Original language	English
Article number	110666
Journal	Applied Soft Computing
Volume	146
DOIs	https://doi.org/10.1016/j.asoc.2023.110666
Publication status	Published (VoR) - Oct 2023

Keywords

Actionability
Convolutional neural networks
Deep learning
Image classification
Medical image analysis
Uncertainty quantification
XAI

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10.1016/j.asoc.2023.110666Licence: CC BY

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@article{71fdafe0c0304baabd1a6df789914fa2,

title = "AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification",

keywords = "Actionability, Convolutional neural networks, Deep learning, Image classification, Medical image analysis, Uncertainty quantification, XAI",

author = "Zakaria Senousy and Gaber, {Mohamed Medhat} and Abdelsamea, {Mohammed M.}",

note = "Funding Information: The authors would like to thank the anonymous reviewers for their invaluable feedback, which significantly contributed to enhancing the overall quality of the paper. Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = oct,

doi = "10.1016/j.asoc.2023.110666",

language = "English",

volume = "146",

journal = "Applied Soft Computing",

issn = "1568-4946",

publisher = "Elsevier",

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T2 - Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

AU - Senousy, Zakaria

AU - Gaber, Mohamed Medhat

AU - Abdelsamea, Mohammed M.

N1 - Funding Information: The authors would like to thank the anonymous reviewers for their invaluable feedback, which significantly contributed to enhancing the overall quality of the paper. Publisher Copyright: © 2023 The Author(s)

PY - 2023/10

Y1 - 2023/10

KW - Actionability

KW - Convolutional neural networks

KW - Deep learning

KW - Image classification

KW - Medical image analysis

KW - Uncertainty quantification

KW - XAI

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DO - 10.1016/j.asoc.2023.110666

M3 - Article

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SN - 1568-4946

VL - 146

JO - Applied Soft Computing

JF - Applied Soft Computing

M1 - 110666

ER -

AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

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