Multi-Scale Part-Based Syndrome Classification of 3D Facial Images

Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Soha Sadat Mahdi [verfasserIn] Harold Matthews [verfasserIn] Nele Nauwelaers [verfasserIn] Michiel Vanneste [verfasserIn] Shunwang Gong [verfasserIn] Giorgos Bouritsas [verfasserIn] Gareth S. Baynam [verfasserIn] Peter Hammond [verfasserIn] Richard Spritz [verfasserIn] Ophir D. Klein [verfasserIn] Benedikt Hallgrimsson [verfasserIn] Hilde Peeters [verfasserIn] Michael Bronstein [verfasserIn] Peter Claes [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 23450-23462
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:23450-23462

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3153357

Katalog-ID:	DOAJ016845692

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allfields	10.1109/ACCESS.2022.3153357 doi (DE-627)DOAJ016845692 (DE-599)DOAJbe55dad4767447a29263c8bcfe7746a7 DE-627 ger DE-627 rakwb eng TK1-9971 Soha Sadat Mahdi verfasserin aut Multi-Scale Part-Based Syndrome Classification of 3D Facial Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics. Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health Electrical engineering. Electronics. Nuclear engineering Harold Matthews verfasserin aut Nele Nauwelaers verfasserin aut Michiel Vanneste verfasserin aut Shunwang Gong verfasserin aut Giorgos Bouritsas verfasserin aut Gareth S. Baynam verfasserin aut Peter Hammond verfasserin aut Richard Spritz verfasserin aut Ophir D. Klein verfasserin aut Benedikt Hallgrimsson verfasserin aut Hilde Peeters verfasserin aut Michael Bronstein verfasserin aut Peter Claes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 23450-23462 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:23450-23462 https://doi.org/10.1109/ACCESS.2022.3153357 kostenfrei https://doaj.org/article/be55dad4767447a29263c8bcfe7746a7 kostenfrei https://ieeexplore.ieee.org/document/9718285/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 23450-23462
spelling	10.1109/ACCESS.2022.3153357 doi (DE-627)DOAJ016845692 (DE-599)DOAJbe55dad4767447a29263c8bcfe7746a7 DE-627 ger DE-627 rakwb eng TK1-9971 Soha Sadat Mahdi verfasserin aut Multi-Scale Part-Based Syndrome Classification of 3D Facial Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics. Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health Electrical engineering. Electronics. Nuclear engineering Harold Matthews verfasserin aut Nele Nauwelaers verfasserin aut Michiel Vanneste verfasserin aut Shunwang Gong verfasserin aut Giorgos Bouritsas verfasserin aut Gareth S. Baynam verfasserin aut Peter Hammond verfasserin aut Richard Spritz verfasserin aut Ophir D. Klein verfasserin aut Benedikt Hallgrimsson verfasserin aut Hilde Peeters verfasserin aut Michael Bronstein verfasserin aut Peter Claes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 23450-23462 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:23450-23462 https://doi.org/10.1109/ACCESS.2022.3153357 kostenfrei https://doaj.org/article/be55dad4767447a29263c8bcfe7746a7 kostenfrei https://ieeexplore.ieee.org/document/9718285/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 23450-23462
allfields_unstemmed	10.1109/ACCESS.2022.3153357 doi (DE-627)DOAJ016845692 (DE-599)DOAJbe55dad4767447a29263c8bcfe7746a7 DE-627 ger DE-627 rakwb eng TK1-9971 Soha Sadat Mahdi verfasserin aut Multi-Scale Part-Based Syndrome Classification of 3D Facial Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics. Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health Electrical engineering. Electronics. Nuclear engineering Harold Matthews verfasserin aut Nele Nauwelaers verfasserin aut Michiel Vanneste verfasserin aut Shunwang Gong verfasserin aut Giorgos Bouritsas verfasserin aut Gareth S. Baynam verfasserin aut Peter Hammond verfasserin aut Richard Spritz verfasserin aut Ophir D. Klein verfasserin aut Benedikt Hallgrimsson verfasserin aut Hilde Peeters verfasserin aut Michael Bronstein verfasserin aut Peter Claes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 23450-23462 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:23450-23462 https://doi.org/10.1109/ACCESS.2022.3153357 kostenfrei https://doaj.org/article/be55dad4767447a29263c8bcfe7746a7 kostenfrei https://ieeexplore.ieee.org/document/9718285/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 23450-23462
allfieldsGer	10.1109/ACCESS.2022.3153357 doi (DE-627)DOAJ016845692 (DE-599)DOAJbe55dad4767447a29263c8bcfe7746a7 DE-627 ger DE-627 rakwb eng TK1-9971 Soha Sadat Mahdi verfasserin aut Multi-Scale Part-Based Syndrome Classification of 3D Facial Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics. Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health Electrical engineering. Electronics. Nuclear engineering Harold Matthews verfasserin aut Nele Nauwelaers verfasserin aut Michiel Vanneste verfasserin aut Shunwang Gong verfasserin aut Giorgos Bouritsas verfasserin aut Gareth S. Baynam verfasserin aut Peter Hammond verfasserin aut Richard Spritz verfasserin aut Ophir D. Klein verfasserin aut Benedikt Hallgrimsson verfasserin aut Hilde Peeters verfasserin aut Michael Bronstein verfasserin aut Peter Claes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 23450-23462 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:23450-23462 https://doi.org/10.1109/ACCESS.2022.3153357 kostenfrei https://doaj.org/article/be55dad4767447a29263c8bcfe7746a7 kostenfrei https://ieeexplore.ieee.org/document/9718285/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 23450-23462
allfieldsSound	10.1109/ACCESS.2022.3153357 doi (DE-627)DOAJ016845692 (DE-599)DOAJbe55dad4767447a29263c8bcfe7746a7 DE-627 ger DE-627 rakwb eng TK1-9971 Soha Sadat Mahdi verfasserin aut Multi-Scale Part-Based Syndrome Classification of 3D Facial Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics. Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health Electrical engineering. Electronics. Nuclear engineering Harold Matthews verfasserin aut Nele Nauwelaers verfasserin aut Michiel Vanneste verfasserin aut Shunwang Gong verfasserin aut Giorgos Bouritsas verfasserin aut Gareth S. Baynam verfasserin aut Peter Hammond verfasserin aut Richard Spritz verfasserin aut Ophir D. Klein verfasserin aut Benedikt Hallgrimsson verfasserin aut Hilde Peeters verfasserin aut Michael Bronstein verfasserin aut Peter Claes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 23450-23462 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:23450-23462 https://doi.org/10.1109/ACCESS.2022.3153357 kostenfrei https://doaj.org/article/be55dad4767447a29263c8bcfe7746a7 kostenfrei https://ieeexplore.ieee.org/document/9718285/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 23450-23462
language	English
source	In IEEE Access 10(2022), Seite 23450-23462 volume:10 year:2022 pages:23450-23462
sourceStr	In IEEE Access 10(2022), Seite 23450-23462 volume:10 year:2022 pages:23450-23462
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Soha Sadat Mahdi @@aut@@ Harold Matthews @@aut@@ Nele Nauwelaers @@aut@@ Michiel Vanneste @@aut@@ Shunwang Gong @@aut@@ Giorgos Bouritsas @@aut@@ Gareth S. Baynam @@aut@@ Peter Hammond @@aut@@ Richard Spritz @@aut@@ Ophir D. Klein @@aut@@ Benedikt Hallgrimsson @@aut@@ Hilde Peeters @@aut@@ Michael Bronstein @@aut@@ Peter Claes @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ016845692
language_de	englisch
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callnumber-first	T - Technology
author	Soha Sadat Mahdi
spellingShingle	Soha Sadat Mahdi misc TK1-9971 misc Clinical genetics misc computer-aided diagnosis misc deep phenotyping misc 3D shape analysis misc geometric deep learning misc precision public health misc Electrical engineering. Electronics. Nuclear engineering Multi-Scale Part-Based Syndrome Classification of 3D Facial Images
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topic_title	TK1-9971 Multi-Scale Part-Based Syndrome Classification of 3D Facial Images Clinical genetics computer-aided diagnosis deep phenotyping 3D shape analysis geometric deep learning precision public health
topic	misc TK1-9971 misc Clinical genetics misc computer-aided diagnosis misc deep phenotyping misc 3D shape analysis misc geometric deep learning misc precision public health misc Electrical engineering. Electronics. Nuclear engineering
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title	Multi-Scale Part-Based Syndrome Classification of 3D Facial Images
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title_full	Multi-Scale Part-Based Syndrome Classification of 3D Facial Images
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author_browse	Soha Sadat Mahdi Harold Matthews Nele Nauwelaers Michiel Vanneste Shunwang Gong Giorgos Bouritsas Gareth S. Baynam Peter Hammond Richard Spritz Ophir D. Klein Benedikt Hallgrimsson Hilde Peeters Michael Bronstein Peter Claes
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title_sort	multi-scale part-based syndrome classification of 3d facial images
callnumber	TK1-9971
title_auth	Multi-Scale Part-Based Syndrome Classification of 3D Facial Images
abstract	Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics.
abstractGer	Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics.
abstract_unstemmed	Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics.
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title_short	Multi-Scale Part-Based Syndrome Classification of 3D Facial Images
url	https://doi.org/10.1109/ACCESS.2022.3153357 https://doaj.org/article/be55dad4767447a29263c8bcfe7746a7 https://ieeexplore.ieee.org/document/9718285/ https://doaj.org/toc/2169-3536
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Multi-Scale Part-Based Syndrome Classification of 3D Facial Images

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