Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer

Rebeca Vétil; Clément Abi-Nader; Alexandre Bône; Marie Pierre Vullierme; Marc Michel Rohé; Pietro Gori; Isabelle Bloch

doi:10.1007/978-3-031-45350-2_6

Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer

Rebeca Vétil
, Clément Abi-Nader
, Alexandre Bône
, Marie Pierre Vullierme
, Marc Michel Rohé
, Pietro Gori
, Isabelle Bloch

Institut Polytechnique de Paris
Guerbet Research
Laboratoire de Probabilités et Modèles Aléatoires
Sorbonne Université

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual information. The resulting DLR features can be combined with hand-crafted ones and leveraged by a classifier to predict early markers of cancer. We illustrate our method on four early markers of pancreatic cancer and validate it on a large independent test set. Our results highlight the value of combining non-redundant DLR and HCR features, as evidenced by an improvement in the Area Under the Curve compared to baseline methods that do not address redundancy or solely rely on HCR features.

langue originale	Anglais
titre	Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings
rédacteurs en chef	Sharib Ali, Fons van der Sommen, Maureen van Eijnatten, Iris Kolenbrander, Bartłomiej W. Papież, Yueming Jin
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	68-82
Nombre de pages	15
ISBN (imprimé)	9783031453496
Les DOIs	https://doi.org/10.1007/978-3-031-45350-2_6
état	Publié - 1 janv. 2023
Evénement	2nd International Workshop on Cancer Prevention through early detecTion, CaPTion 2023 - Vancover, Canada Durée: 12 oct. 2023 → 12 oct. 2023

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14295 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	2nd International Workshop on Cancer Prevention through early detecTion, CaPTion 2023
Pays/Territoire	Canada
La ville	Vancover
période	12/10/23 → 12/10/23

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 3 Bonne santé et bien-être

Accès au document

10.1007/978-3-031-45350-2_6

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

Vétil, R., Abi-Nader, C., Bône, A., Vullierme, M. P., Rohé, M. M., Gori, P., & Bloch, I. (2023). Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer. Dans S. Ali, F. van der Sommen, M. van Eijnatten, I. Kolenbrander, B. W. Papież, & Y. Jin (eds.), Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings (p. 68-82). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 14295 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-45350-2_6

Vétil, Rebeca ; Abi-Nader, Clément ; Bône, Alexandre et al. / Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics : Application to the Early Detection of Pancreatic Cancer. Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings. Editeur / Sharib Ali ; Fons van der Sommen ; Maureen van Eijnatten ; Iris Kolenbrander ; Bartłomiej W. Papież ; Yueming Jin. Springer Science and Business Media Deutschland GmbH, 2023. p. 68-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer",

abstract = "We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual information. The resulting DLR features can be combined with hand-crafted ones and leveraged by a classifier to predict early markers of cancer. We illustrate our method on four early markers of pancreatic cancer and validate it on a large independent test set. Our results highlight the value of combining non-redundant DLR and HCR features, as evidenced by an improvement in the Area Under the Curve compared to baseline methods that do not address redundancy or solely rely on HCR features.",

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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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Vétil, R, Abi-Nader, C, Bône, A, Vullierme, MP, Rohé, MM, Gori, P & Bloch, I 2023, Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer. Dans S Ali, F van der Sommen, M van Eijnatten, I Kolenbrander, BW Papież & Y Jin (eds), Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 14295 LNCS, Springer Science and Business Media Deutschland GmbH, p. 68-82, 2nd International Workshop on Cancer Prevention through early detecTion, CaPTion 2023, Vancover, Canada, 12/10/23. https://doi.org/10.1007/978-3-031-45350-2_6

Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer. / Vétil, Rebeca; Abi-Nader, Clément; Bône, Alexandre et al.
Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings. Ed. / Sharib Ali; Fons van der Sommen; Maureen van Eijnatten; Iris Kolenbrander; Bartłomiej W. Papież; Yueming Jin. Springer Science and Business Media Deutschland GmbH, 2023. p. 68-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 14295 LNCS).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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T2 - 2nd International Workshop on Cancer Prevention through early detecTion, CaPTion 2023

AU - Vétil, Rebeca

AU - Abi-Nader, Clément

AU - Bône, Alexandre

AU - Vullierme, Marie Pierre

AU - Rohé, Marc Michel

AU - Gori, Pietro

AU - Bloch, Isabelle

PY - 2023/1/1

Y1 - 2023/1/1

N2 - We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual information. The resulting DLR features can be combined with hand-crafted ones and leveraged by a classifier to predict early markers of cancer. We illustrate our method on four early markers of pancreatic cancer and validate it on a large independent test set. Our results highlight the value of combining non-redundant DLR and HCR features, as evidenced by an improvement in the Area Under the Curve compared to baseline methods that do not address redundancy or solely rely on HCR features.

AB - We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual information. The resulting DLR features can be combined with hand-crafted ones and leveraged by a classifier to predict early markers of cancer. We illustrate our method on four early markers of pancreatic cancer and validate it on a large independent test set. Our results highlight the value of combining non-redundant DLR and HCR features, as evidenced by an improvement in the Area Under the Curve compared to baseline methods that do not address redundancy or solely rely on HCR features.

KW - Early Diagnosis

KW - Mutual Information

KW - Pancreatic Cancer

KW - Radiomics

KW - Variational Autoencoders

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U2 - 10.1007/978-3-031-45350-2_6

DO - 10.1007/978-3-031-45350-2_6

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AN - SCOPUS:85175983539

SN - 9783031453496

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

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BT - Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings

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PB - Springer Science and Business Media Deutschland GmbH

Y2 - 12 October 2023 through 12 October 2023

ER -

Vétil R, Abi-Nader C, Bône A, Vullierme MP, Rohé MM, Gori P et al. Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer. Dans Ali S, van der Sommen F, van Eijnatten M, Kolenbrander I, Papież BW, Jin Y, rédacteurs en chef, Cancer Prevention Through Early Detection - 2nd International Workshop, CaPTion 2023, Held in Conjunction with MICCAI 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 68-82. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-45350-2_6