Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 183; pp. 504 - 521 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.12.2018
Elsevier Limited Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2018.08.042 |
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| Abstract | A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML. |
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| AbstractList | A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML. A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML. A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML.A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML. |
| Author | Routier, Alexandre Samper-González, Jorge Colliot, Olivier Bertrand, Anne Habert, Marie-Odile Evgeniou, Theodoros Wen, Junhao Burgos, Ninon Fontanella, Sabrina Bertin, Hugo Lu, Pascal Marcoux, Arnaud Bottani, Simona Bacci, Michael Durrleman, Stanley Guillon, Jérémy |
| Author_xml | – sequence: 1 givenname: Jorge surname: Samper-González fullname: Samper-González, Jorge email: jorge.samper-gonzalez@inria.fr organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 2 givenname: Ninon surname: Burgos fullname: Burgos, Ninon organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 3 givenname: Simona surname: Bottani fullname: Bottani, Simona organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 4 givenname: Sabrina surname: Fontanella fullname: Fontanella, Sabrina organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 5 givenname: Pascal surname: Lu fullname: Lu, Pascal organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 6 givenname: Arnaud surname: Marcoux fullname: Marcoux, Arnaud organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 7 givenname: Alexandre surname: Routier fullname: Routier, Alexandre organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 8 givenname: Jérémy surname: Guillon fullname: Guillon, Jérémy organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 9 givenname: Michael surname: Bacci fullname: Bacci, Michael organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 10 givenname: Junhao surname: Wen fullname: Wen, Junhao organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 11 givenname: Anne surname: Bertrand fullname: Bertrand, Anne organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 12 givenname: Hugo surname: Bertin fullname: Bertin, Hugo organization: Laboratoire d'Imagerie Biomédicale, Inserm, U 1146, CNRS, UMR 7371, Sorbonne Université, F-75013, Paris, France – sequence: 13 givenname: Marie-Odile surname: Habert fullname: Habert, Marie-Odile organization: Laboratoire d'Imagerie Biomédicale, Inserm, U 1146, CNRS, UMR 7371, Sorbonne Université, F-75013, Paris, France – sequence: 14 givenname: Stanley surname: Durrleman fullname: Durrleman, Stanley organization: Inria, ARAMIS Project-team, F-75013, Paris, France – sequence: 15 givenname: Theodoros surname: Evgeniou fullname: Evgeniou, Theodoros organization: INSEAD, Bd de Constance, 77305, Fontainebleau, France – sequence: 16 givenname: Olivier surname: Colliot fullname: Colliot, Olivier email: olivier.colliot@upmc.fr organization: Inria, ARAMIS Project-team, F-75013, Paris, France |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30130647$$D View this record in MEDLINE/PubMed https://inria.hal.science/hal-01858384$$DView record in HAL |
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| Copyright | 2018 Elsevier Inc. Copyright © 2018 Elsevier Inc. All rights reserved. 2018. Elsevier Inc. Attribution |
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| Keywords | Open-source Magnetic resonance imaging Reproducibility Alzheimer's disease Positron emission tomography Classification |
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| SubjectTerms | Accuracy Algorithms Alzheimer's disease Artificial intelligence Biomarkers Classification Cognitive science Computer Science Data processing Datasets Experiments Learning algorithms Machine learning Magnetic resonance imaging Medical imaging Neurodegenerative diseases Neuroscience NMR Nuclear magnetic resonance Open-source Positron emission tomography Reproducibility Studies |
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| Title | Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data |
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