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16 results on '"Hassanaly, Ravi"'

1. Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

Author

Hassanaly, Ravi, Brianceau, Camille, Solal, Maëlys, Colliot, Olivier, and Burgos, Ninon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity. This approach has the great advantage of not requiring tedious pixel-wise data annotation and offers possibility to generalize to any kind of anomalies, including that corresponding to rare diseases. By training a deep generative model with only images from healthy subjects, the model will learn to reconstruct pseudo-healthy images. This pseudo-healthy reconstruction is then compared to the input to detect and localize anomalies. The evaluation of such methods often relies on a ground truth lesion mask that is available for test data, which may not exist depending on the application. We propose an evaluation procedure based on the simulation of realistic abnormal images to validate pseudo-healthy reconstruction methods when no ground truth is available. This allows us to extensively test generative models on different kinds of anomalies and measuring their performance using the pair of normal and abnormal images corresponding to the same subject. It can be used as a preliminary automatic step to validate the capacity of a generative model to reconstruct pseudo-healthy images, before a more advanced validation step that would require clinician's expertise. We apply this framework to the reconstruction of 3D brain FDG PET using a convolutional variational autoencoder with the aim to detect as early as possible the neurodegeneration markers that are specific to dementia such as Alzheimer's disease., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:003

Published
2024
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2. Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

Author

Solal, Maëlys, Hassanaly, Ravi, and Burgos, Ninon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data. It relies on building a subject-specific model of healthy appearance to which a subject's image can be compared to detect anomalies. In the literature, it is common for anomaly detection to rely on analysing the residual image between the subject's image and its pseudo-healthy reconstruction. This approach however has limitations partly due to the pseudo-healthy reconstructions being imperfect and to the lack of natural thresholding mechanism. Our proposed method, inspired by Z-scores, leverages the healthy population variability to overcome these limitations. Our experiments conducted on FDG PET scans from the ADNI database demonstrate the effectiveness of our approach in accurately identifying Alzheimer's disease related anomalies., Comment: SPIE Medical Imaging, Feb 2024, San Diego (California), United States

Published
2023

3. Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches

Author

Hassanaly, Ravi, Brianceau, Camille, Colliot, Olivier, Burgos, Ninon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mukhopadhyay, Anirban, editor, Oksuz, Ilkay, editor, Engelhardt, Sandy, editor, Zhu, Dajiang, editor, and Yuan, Yixuan, editor

Published
2024
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5. Clinica: an open source software platform for reproducible clinical neuroscience studies

Author

Routier, Alexandre, Burgos, Ninon, Díaz, Mauricio, Bacci, Michael, Bottani, Simona, El-Rifai, Omar, Fontanella, Sabrina, Gori, Pietro, Guillon, Jérémy, Guyot, Alexis, Hassanaly, Ravi, Jacquemont, Thomas, Lu, Pascal, Marcoux, Arnaud, Moreau, Tristan, Samper-González, Jorge, Teichmann, Marc, Thibeau--Sutre, Elina, Vaillant, Ghislain, Wen, Junhao, Wild, Adam, Habert, Marie-Odile, Durrleman, Stanley, and Colliot, Olivier

Subjects
Quantitative Biology - Quantitative Methods
Abstract

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to i) spend less time on data management and processing, ii) perform reproducible evaluations of their methods, and iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI and PET data), as well as tools for statistics, machine learning and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS and NIFD). Processed data include image-valued scalar fields (e.g. tissue probability maps), meshes, surface-based scalar fields (e.g. cortical thickness maps) or scalar outputs (e.g. regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Published
2021

6. Main Existing Datasets for Open Brain Research on Humans

Author

Couvy-Duchesne, Baptiste, primary, Bottani, Simona, additional, Camenen, Etienne, additional, Fang, Fang, additional, Fikere, Mulusew, additional, Gonzalez-Astudillo, Juliana, additional, Harvey, Joshua, additional, Hassanaly, Ravi, additional, Kassam, Irfahan, additional, Lind, Penelope A., additional, Liu, Qianwei, additional, Lu, Yi, additional, Nabais, Marta, additional, Rolland, Thibault, additional, Sidorenko, Julia, additional, Strike, Lachlan, additional, and Wright, Margie, additional

Published
2023
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11. Main existing datasets for open data research on humans

Author

Couvy-Duchesne, Baptiste, Bottani, Simona, Camenen, Etienne, Fang, Fang, Fikere, Mulusew, Gonzalez-Astudillo, Juliana, Harvey, Joshua, Hassanaly, Ravi, Kassam, Irfahan, Lind, Penelope, Liu, Qianwei, Lu, Yi, Nabais, Marta, Rolland, Thibault, Sidorenko, Julia, Strike, Lachlan, Wright, Margie, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institute for Molecular Bioscience, University of Queensland [Brisbane], Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Karolinska Institutet [Stockholm], University of Exeter Medical School, University of Exeter, Lee Kong Chian School of Medicine, Nanyang Technological University [Singapour], QIMR Berghofer Medical Research Institute, Queensland Brain Institute, This research was supported by the Australian National Health and Medical Research Council (1078037, 1078901, 1113400, 1161356 and 1107258), the Australian Research Council (FT180100186 and FL180100072), the Sylvia & Charles Viertel Charitable Foundation, the program 'Investissements d’avenir' ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6) and reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), the European Union H2020 program (project EuroPOND, grant number 666992, the joint NSF/NIH/ANR program 'Collaborative Research in Computational Neuroscience' (project HIPLAY7, grant number ANR-16-NEUC-0001-01), the ICM Big Brain Theory Program (project DYNAMO, project PredictICD), and the Abeona Foundation (project Brain@Scale). BCD is supported by a CJ Martin fellowship (APP1161356)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-16-NEUC-0001,HIPLAY7,Experimental and theoretical investigation of the multi-scale emergence of parametric working memory in prefrontal cortex recurrent networks(2016), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), Couvy-Duchesne, Baptiste, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Experimental and theoretical investigation of the multi-scale emergence of parametric working memory in prefrontal cortex recurrent networks - - HIPLAY72016 - ANR-16-NEUC-0001 - CRCNS - VALID, and Data-driven models for Progression Of Neurological Disease - EuroPOND - - H2020 Pilier Societal Challenges2016-01-01 - 2019-12-31 - 666992 - VALID

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, PET, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], EEG/MEG, Brain MRI, Wearables, Genetics, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Electronic health records, Omics, [SDV.GEN] Life Sciences [q-bio]/Genetics, Gene expression, Methylation
Abstract

Recent advances in technology have made possible to quantify fine-grained individual differences at many levels, such as genetic, genomics, organ level, behaviour, clinical. The wealth of data becoming available raises great promises for research on brain disorders as well as normal brain function. To name a few: systematic and agnostic study of disease risk factors (e.g. genetic variants, brain regions), the use of natural experiments (e.g. evaluate the effect of a genetic variant in a human population), and unveiling disease mechanisms across several biological levels (e.g. genetics, cellular gene expression, organ structure and function). However, this data revolution raises many challenges such as data sharing and management, the need for novel analysis methods and software, storage and computing. Here, we sought to provide an overview of some of the main existing human datasets, all accessible to researchers. Our list is far from being exhaustive and our objective is to publicise data sharing initiatives and help researchers find new data sources.

Published
2022

12. Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

Author

Routier, Alexandre, primary, Burgos, Ninon, additional, Díaz, Mauricio, additional, Bacci, Michael, additional, Bottani, Simona, additional, El-Rifai, Omar, additional, Fontanella, Sabrina, additional, Gori, Pietro, additional, Guillon, Jérémy, additional, Guyot, Alexis, additional, Hassanaly, Ravi, additional, Jacquemont, Thomas, additional, Lu, Pascal, additional, Marcoux, Arnaud, additional, Moreau, Tristan, additional, Samper-González, Jorge, additional, Teichmann, Marc, additional, Thibeau-Sutre, Elina, additional, Vaillant, Ghislain, additional, Wen, Junhao, additional, Wild, Adam, additional, Habert, Marie-Odile, additional, Durrleman, Stanley, additional, and Colliot, Olivier, additional

Published
2021
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16. Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PET

Author

Ravi Hassanaly, Simona Bottani, Benoît Sauty, Olivier Colliot, Ninon Burgos, HASSANALY, Ravi, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Deep generative models, Simulation framework, [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine, Unsupervised anomaly detection, Alzheimer's disease, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [SDV.IB.MN] Life Sciences [q-bio]/Bioengineering/Nuclear medicine, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], PET, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; Unsupervised anomaly detection using deep learning models is a popular computer-aided diagnosis approach because it does not need annotated data and is not restricted to the diagnosis of a disease seen during training. Such approach consists in first learning the distribution of anomaly free images. Images presenting anomalies are then detected as outliers of this distribution. These approaches have been widely applied in neuroimaging to detect sharp and localized anomalies such as tumors or white matter hyper-intensities from structural MRI. In this work, we aim to detect anomalies from FDG PET images of patients with Alzheimer's disease. In this context, the anomalies can be subtle and difficult to delineate, making the task more difficult and meaning that no ground truth exists to evaluate the approaches. We thus propose a framework to evaluate unsupervised anomaly detection approaches that consists in simulating realistic anomalies from images of healthy subjects. We demonstrate the use of this framework by evaluating an approach based on a 3D variational autoencoder.

Published
2023

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