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Adaptive Feature Selection Using an Autoencoder and Classifier: Applied to a Radiomics Case

Authors:

Reza Hassanpour,

Mortaza Shoae Bargh,

Sunil ChoenniAuthors Info & Claims

SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

Pages 1256 - 1259

https://doi.org/10.1145/3555776.3577861

Published: 07 June 2023 Publication History

Abstract

Machine learning models have been an inevitable tool for analyzing medical images by radiologists. These models provide important information about the contents of these images using extracted radiomic features. However, the dimensionality of the feature space can cause reduction in the accuracy of prediction, a phenomenon known as the curse of dimensionality. In this study we propose a feature selection method using an autoencoder, which incorporates the performance of a classifier within the feature selection process. This is achieved by automatically adjusting a threshold value used for selecting the features fed to the classifier. The contribution of this study is twofold. The first contribution is an improvement to group lasso to include the group size as a cost parameter of the autoencoder. The second contribution is to automate the selection of the threshold value used for eliminating redundant input features. The threshold value in our proposed method is learned during training phase of the proposed model. Our experimental results indicates that the proposed model can successfully converge to appropriate feature selection parameters.

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Cited By

Mylona EZaridis DKalantzopoulos CTachos NRegge DPapanikolaou NTsiknakis MMarias KMylona EZaridis DKalantzopoulos CTachos NRegge DPapanikolaou NTsiknakis MMarias KFotiadis DSfakianakis SKalokyri VTrivizakis EKalliatakis GDimitriadis Ade Almeida JVerde ARodrigues ARodrigues NChambel MHuisman Hde Rooij MSaha ATwilt JFutterer JMartí-Bonmatí LCerdá-Alberich LRibas GNavarro SMarfil MNeri EAringhieri GTumminello LMendola Vnan Akata DÖzmen MKaraosmanoglu AAtak FKarcaaltincaba MVilanova JUsinskiene JBriediene RUntanas ASlidevska KVasilis KGeorgios GKoh DEmsley RVit SRibeiro ADoran SJacobs TGarcía-Martí GGiannini VMazzetti SCappello GMaimone GNapolitano VColantonio SPascali MPachetti Edel Corso GGermanese DBerti ACarloni GKalpathy-Cramer JBridge CCorreia JHernandez WGiavri ZPollalis CAgraniotis DPastor AMora JSaillant CHenne TMarquez RFotiadis D(2024)Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequencesInsights into Imaging10.1186/s13244-024-01783-915:1Online publication date: 4-Nov-2024
https://doi.org/10.1186/s13244-024-01783-9
Sowmya VBharathi Malakreddy ANatarajan SPrathik NManjunath S(2024)Machine Learning Approaches for Brain Tumor Classification in Multimodal MR ImagesBusiness Sustainability with Artificial Intelligence (AI): Challenges and Opportunities10.1007/978-3-031-71526-6_13(137-152)Online publication date: 25-Dec-2024
https://doi.org/10.1007/978-3-031-71526-6_13
Sowmya VMalakreddy ANatarajan SPrathik N(2024)Advancing Glioblastoma Treatment Through AI-Driven Radiomics: A Comparative Study of Feature Selection and Machine Learning TechniquesRevolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes10.1007/978-3-031-65022-2_4(43-62)Online publication date: 24-Sep-2024
https://doi.org/10.1007/978-3-031-65022-2_4

Index Terms

Adaptive Feature Selection Using an Autoencoder and Classifier: Applied to a Radiomics Case
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Learning from implicit feedback

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Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

March 2023

1932 pages

ISBN:9781450395175

DOI:10.1145/3555776

Conference Chairs:
Jiman Hong
Soongsil University, South Korea
,
Maart Lanperne
Tallinn University, Estonia
,
Program Chairs:
Juw Won Park
University of Louisville, USA
,
Tomas Cerny
Baylor University, USA
,
Publication Chair:
Hossain Shahriar
Kennesaw State University, USA

Copyright © 2023 Owner/Author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2023

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SAC '23

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SIGAPP

SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing

March 27 - 31, 2023

Tallinn, Estonia

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Mylona EZaridis DKalantzopoulos CTachos NRegge DPapanikolaou NTsiknakis MMarias KMylona EZaridis DKalantzopoulos CTachos NRegge DPapanikolaou NTsiknakis MMarias KFotiadis DSfakianakis SKalokyri VTrivizakis EKalliatakis GDimitriadis Ade Almeida JVerde ARodrigues ARodrigues NChambel MHuisman Hde Rooij MSaha ATwilt JFutterer JMartí-Bonmatí LCerdá-Alberich LRibas GNavarro SMarfil MNeri EAringhieri GTumminello LMendola Vnan Akata DÖzmen MKaraosmanoglu AAtak FKarcaaltincaba MVilanova JUsinskiene JBriediene RUntanas ASlidevska KVasilis KGeorgios GKoh DEmsley RVit SRibeiro ADoran SJacobs TGarcía-Martí GGiannini VMazzetti SCappello GMaimone GNapolitano VColantonio SPascali MPachetti Edel Corso GGermanese DBerti ACarloni GKalpathy-Cramer JBridge CCorreia JHernandez WGiavri ZPollalis CAgraniotis DPastor AMora JSaillant CHenne TMarquez RFotiadis D(2024)Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequencesInsights into Imaging10.1186/s13244-024-01783-915:1Online publication date: 4-Nov-2024
https://doi.org/10.1186/s13244-024-01783-9
Sowmya VBharathi Malakreddy ANatarajan SPrathik NManjunath S(2024)Machine Learning Approaches for Brain Tumor Classification in Multimodal MR ImagesBusiness Sustainability with Artificial Intelligence (AI): Challenges and Opportunities10.1007/978-3-031-71526-6_13(137-152)Online publication date: 25-Dec-2024
https://doi.org/10.1007/978-3-031-71526-6_13
Sowmya VMalakreddy ANatarajan SPrathik N(2024)Advancing Glioblastoma Treatment Through AI-Driven Radiomics: A Comparative Study of Feature Selection and Machine Learning TechniquesRevolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes10.1007/978-3-031-65022-2_4(43-62)Online publication date: 24-Sep-2024
https://doi.org/10.1007/978-3-031-65022-2_4

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