Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification

Yingjie Feng; Wei Chen; Xianfeng Gu; Xiaoyin Xu; Min Zhang

doi:10.1007/978-3-031-43907-0_13

Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification

Yingjie Feng
, Wei Chen
, Xianfeng Gu
, Xiaoyin Xu
, Min Zhang

Computer Science

Zhejiang University
Sir Run Run Shaw Hospital
Harvard University

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

7 Scopus citations

Abstract

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, so early identification of Alzheimer’s disease and its early stage disorder, mild cognitive impairment (MCI), is of great significance. However, currently available labeled datasets are still small, so the development of semi-supervised classification algorithms will be beneficial for clinical applications. We propose a novel uncertainty-aware semi-supervised learning framework based on the improved evidential regression. Our framework uses the aleatoric uncertainty (AU) from the data itself and the epistemic uncertainty (EU) from the model to optimize the evidential classifier and feature extractor step by step to achieve the best performance close to supervised learning with small labeled data counts. We conducted various experiments on the ADNI-2 dataset, demonstrating the effectiveness and advancement of our method.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
Editors	Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	130-140
Number of pages	11
ISBN (Print)	9783031439063
DOIs	https://doi.org/10.1007/978-3-031-43907-0_13
State	Published - 2023
Event	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada Duration: Oct 8 2023 → Oct 12 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14220 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/Territory	Canada
City	Vancouver
Period	10/8/23 → 10/12/23

Keywords

Alzheimer’s disease
Deep evidential regression
EfficientNet-V2
Multi-modality
Semi-supervised learning

Access to Document

10.1007/978-3-031-43907-0_13

Cite this

Feng, Y., Chen, W., Gu, X., Xu, X., & Zhang, M. (2023). Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification. In H. Greenspan, H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings (pp. 130-140). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14220 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43907-0_13

Feng, Yingjie ; Chen, Wei ; Gu, Xianfeng et al. / Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. editor / Hayit Greenspan ; Hayit Greenspan ; Anant Madabhushi ; Parvin Mousavi ; Septimiu Salcudean ; James Duncan ; Tanveer Syeda-Mahmood ; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. pp. 130-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Alzheimer{\textquoteright}s disease (AD) is an irreversible neurodegenerative disease, so early identification of Alzheimer{\textquoteright}s disease and its early stage disorder, mild cognitive impairment (MCI), is of great significance. However, currently available labeled datasets are still small, so the development of semi-supervised classification algorithms will be beneficial for clinical applications. We propose a novel uncertainty-aware semi-supervised learning framework based on the improved evidential regression. Our framework uses the aleatoric uncertainty (AU) from the data itself and the epistemic uncertainty (EU) from the model to optimize the evidential classifier and feature extractor step by step to achieve the best performance close to supervised learning with small labeled data counts. We conducted various experiments on the ADNI-2 dataset, demonstrating the effectiveness and advancement of our method.",

keywords = "Alzheimer{\textquoteright}s disease, Deep evidential regression, EfficientNet-V2, Multi-modality, Semi-supervised learning",

author = "Yingjie Feng and Wei Chen and Xianfeng Gu and Xiaoyin Xu and Min Zhang",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 ; Conference date: 08-10-2023 Through 12-10-2023",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Hayit Greenspan and Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor",

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Feng, Y, Chen, W, Gu, X, Xu, X & Zhang, M 2023, Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification. in H Greenspan, H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14220 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 130-140, 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada, 10/8/23. https://doi.org/10.1007/978-3-031-43907-0_13

Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification. / Feng, Yingjie; Chen, Wei; Gu, Xianfeng et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. ed. / Hayit Greenspan; Hayit Greenspan; Anant Madabhushi; Parvin Mousavi; Septimiu Salcudean; James Duncan; Tanveer Syeda-Mahmood; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. p. 130-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14220 LNCS).

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

TY - GEN

T1 - Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification

AU - Feng, Yingjie

AU - Chen, Wei

AU - Gu, Xianfeng

AU - Xu, Xiaoyin

AU - Zhang, Min

PY - 2023

Y1 - 2023

N2 - Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, so early identification of Alzheimer’s disease and its early stage disorder, mild cognitive impairment (MCI), is of great significance. However, currently available labeled datasets are still small, so the development of semi-supervised classification algorithms will be beneficial for clinical applications. We propose a novel uncertainty-aware semi-supervised learning framework based on the improved evidential regression. Our framework uses the aleatoric uncertainty (AU) from the data itself and the epistemic uncertainty (EU) from the model to optimize the evidential classifier and feature extractor step by step to achieve the best performance close to supervised learning with small labeled data counts. We conducted various experiments on the ADNI-2 dataset, demonstrating the effectiveness and advancement of our method.

AB - Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, so early identification of Alzheimer’s disease and its early stage disorder, mild cognitive impairment (MCI), is of great significance. However, currently available labeled datasets are still small, so the development of semi-supervised classification algorithms will be beneficial for clinical applications. We propose a novel uncertainty-aware semi-supervised learning framework based on the improved evidential regression. Our framework uses the aleatoric uncertainty (AU) from the data itself and the epistemic uncertainty (EU) from the model to optimize the evidential classifier and feature extractor step by step to achieve the best performance close to supervised learning with small labeled data counts. We conducted various experiments on the ADNI-2 dataset, demonstrating the effectiveness and advancement of our method.

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KW - Multi-modality

KW - Semi-supervised learning

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DO - 10.1007/978-3-031-43907-0_13

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AN - SCOPUS:85174630265

SN - 9783031439063

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 130

EP - 140

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings

A2 - Greenspan, Hayit

A2 - Madabhushi, Anant

A2 - Mousavi, Parvin

A2 - Salcudean, Septimiu

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A2 - Syeda-Mahmood, Tanveer

A2 - Taylor, Russell

PB - Springer Science and Business Media Deutschland GmbH

T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023

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Feng Y, Chen W, Gu X, Xu X, Zhang M. Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification. In Greenspan H, Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 130-140. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43907-0_13