Survey on machine learning applied to medical image analysis

Aurelien  Olivier; Clement Hoffmann; Ali Mansour; Luc Bressollette; Benoit Clement

doi:10.1109/CISP-BMEI53629.2021.9624442

Survey on machine learning applied to medical image analysis

Aurelien Olivier, Clement Hoffmann, Ali Mansour, Luc Bressollette, Benoit Clement

College of Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a selective survey on recent advances in machine learning applied to medical imaging. It aims to highlight both innovations that increase the performance of the models and methods that ensure certainty, interpretability and robustness of the trained models. The paper focuses particularly on new concepts such as attention modules that allow to gather specific features considering global context. Its second main focus is given to domain adaptation methods to enhance model robustness to distribution shifts. Finally, we discuss uncertainty estimation and interpretability methods to evaluate confidence in a trained model.

Original language	English
Title of host publication	Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Editors	Qingli Li, Lipo Wang, Yan Wang, Wenwu Li
Place of Publication	Online
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Electronic)	9781665400046, 9781665400039, 9781665400053
DOIs	https://doi.org/10.1109/CISP-BMEI53629.2021.9624442
Publication status	Published - 2021
Event	International Congress on Image and Signal Processing, BioMedical Engineering and Informatics - Shanghai, China Duration: 23 Oct 2021 → … Conference number: 14

Publication series

Name	Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021

Conference

Conference	International Congress on Image and Signal Processing, BioMedical Engineering and Informatics
Abbreviated title	CISP-BMEI
Country/Territory	China
City	Shanghai
Period	23/10/21 → …

Keywords

Deep learning
Machine learning
Medical image analysis
attention modules
domain adaptation
uncertainty estimation
venous Thrombo-Embolism

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10.1109/CISP-BMEI53629.2021.9624442Licence: In Copyright

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Olivier, A., Hoffmann, C., Mansour, A., Bressollette, L., & Clement, B. (2021). Survey on machine learning applied to medical image analysis. In Q. Li, L. Wang, Y. Wang, & W. Li (Eds.), Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021 (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CISP-BMEI53629.2021.9624442

Olivier, Aurelien ; Hoffmann, Clement ; Mansour, Ali et al. / Survey on machine learning applied to medical image analysis. Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. editor / Qingli Li ; Lipo Wang ; Yan Wang ; Wenwu Li. Online : Institute of Electrical and Electronics Engineers, 2021. (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021).

@inproceedings{27522156f0c04b74be3f507736c82710,

title = "Survey on machine learning applied to medical image analysis",

abstract = "This paper presents a selective survey on recent advances in machine learning applied to medical imaging. It aims to highlight both innovations that increase the performance of the models and methods that ensure certainty, interpretability and robustness of the trained models. The paper focuses particularly on new concepts such as attention modules that allow to gather specific features considering global context. Its second main focus is given to domain adaptation methods to enhance model robustness to distribution shifts. Finally, we discuss uncertainty estimation and interpretability methods to evaluate confidence in a trained model.",

keywords = "Deep learning, Machine learning, Medical image analysis, attention modules, domain adaptation, uncertainty estimation, venous Thrombo-Embolism",

author = "Aurelien Olivier and Clement Hoffmann and Ali Mansour and Luc Bressollette and Benoit Clement",

year = "2021",

doi = "10.1109/CISP-BMEI53629.2021.9624442",

language = "English",

series = "Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021",

publisher = "Institute of Electrical and Electronics Engineers",

editor = "Qingli Li and Lipo Wang and Yan Wang and Wenwu Li",

booktitle = "Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021",

address = "United States",

note = "International Congress on Image and Signal Processing, BioMedical Engineering and Informatics , CISP-BMEI ; Conference date: 23-10-2021",

}

Olivier, A, Hoffmann, C, Mansour, A, Bressollette, L & Clement, B 2021, Survey on machine learning applied to medical image analysis. in Q Li, L Wang, Y Wang & W Li (eds), Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021, Institute of Electrical and Electronics Engineers, Online, International Congress on Image and Signal Processing, BioMedical Engineering and Informatics , Shanghai, China, 23/10/21. https://doi.org/10.1109/CISP-BMEI53629.2021.9624442

Survey on machine learning applied to medical image analysis. / Olivier, Aurelien ; Hoffmann, Clement; Mansour, Ali et al.
Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. ed. / Qingli Li; Lipo Wang; Yan Wang; Wenwu Li. Online: Institute of Electrical and Electronics Engineers, 2021. (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Survey on machine learning applied to medical image analysis

AU - Olivier, Aurelien

AU - Hoffmann, Clement

AU - Mansour, Ali

AU - Bressollette, Luc

AU - Clement, Benoit

N1 - Conference code: 14

PY - 2021

Y1 - 2021

N2 - This paper presents a selective survey on recent advances in machine learning applied to medical imaging. It aims to highlight both innovations that increase the performance of the models and methods that ensure certainty, interpretability and robustness of the trained models. The paper focuses particularly on new concepts such as attention modules that allow to gather specific features considering global context. Its second main focus is given to domain adaptation methods to enhance model robustness to distribution shifts. Finally, we discuss uncertainty estimation and interpretability methods to evaluate confidence in a trained model.

AB - This paper presents a selective survey on recent advances in machine learning applied to medical imaging. It aims to highlight both innovations that increase the performance of the models and methods that ensure certainty, interpretability and robustness of the trained models. The paper focuses particularly on new concepts such as attention modules that allow to gather specific features considering global context. Its second main focus is given to domain adaptation methods to enhance model robustness to distribution shifts. Finally, we discuss uncertainty estimation and interpretability methods to evaluate confidence in a trained model.

KW - Deep learning

KW - Machine learning

KW - Medical image analysis

KW - attention modules

KW - domain adaptation

KW - uncertainty estimation

KW - venous Thrombo-Embolism

UR - http://www.scopus.com/inward/record.url?scp=85123473845&partnerID=8YFLogxK

U2 - 10.1109/CISP-BMEI53629.2021.9624442

DO - 10.1109/CISP-BMEI53629.2021.9624442

M3 - Conference contribution

T3 - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021

BT - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021

A2 - Li, Qingli

A2 - Wang, Lipo

A2 - Wang, Yan

A2 - Li, Wenwu

PB - Institute of Electrical and Electronics Engineers

CY - Online

T2 - International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

Y2 - 23 October 2021

ER -

Olivier A, Hoffmann C, Mansour A, Bressollette L, Clement B. Survey on machine learning applied to medical image analysis. In Li Q, Wang L, Wang Y, Li W, editors, Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. Online: Institute of Electrical and Electronics Engineers. 2021. (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021). doi: 10.1109/CISP-BMEI53629.2021.9624442

Survey on machine learning applied to medical image analysis

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