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

8 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

Funding

The authors would like to thank the anonymous reviewers for their invaluable feedback, which significantly contributed to enhancing the overall quality of the paper.

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

UR - https://www.scopus.com/pages/publications/85169928222

UR - https://www.scopus.com/inward/citedby.url?scp=85169928222&partnerID=8YFLogxK

UR - https://www.open-access.bcu.ac.uk/14826/

U2 - 10.1016/j.asoc.2023.110666

DO - 10.1016/j.asoc.2023.110666

M3 - Article

AN - SCOPUS:85169928222

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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Keywords

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