MELBA – Publications

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- December 2020 issue

June 2024 issue

https://doi.org/10.59275/j.melba.2024-55f5

3 papers

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation cover file

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation

2024/06/23
June 2024 issue

Yidong ZhaoDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands, João TouraisDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands, Iain PierceBarts Heart Centre, Barts Health NHS Trust, London, United Kingdom
Institute of Cardiovascular Science, University College, London, United Kingdom, Christian NitscheBarts Heart Centre, Barts Health NHS Trust, London, United Kingdom
Institute of Cardiovascular Science, University College, London, United Kingdom, Thomas A. TreibelBarts Heart Centre, Barts Health NHS Trust, London, United Kingdom
Institute of Cardiovascular Science, University College, London, United Kingdom, Sebastian WeingärtnerDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands, Artur M. SchweidtmannDepartment of Chemical Engineering, Delft University of Technology, Delft, The Netherlands, Qian TaoDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands

PDF · Code · arXiv

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation cover file

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation

2024/06/14
June 2024 issue

Sophie LoizillonSorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France et al.

Sophie LoizillonSorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France, Simona BottaniSorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France, Stéphane MabilleSorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France, Yannick JacobAP-HP, WIND department,Paris 75012, France, Aurélien MaireAP-HP, WIND department,Paris 75012, France, Sebastian StröerAP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris 75013, France, Didier DormontSorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, DMU DIAMENT, Paris 75013, France, Olivier ColliotSorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France, Ninon BurgosSorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France, The Alzheimer’s Disease Neuroimaging Initiative http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf, APPRIMAGE Study Group https://www.aramislab.fr/apprimage

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Deep Monte-Carlo EM for Semantic Segmentation using Weakly-and-Semi-Supervised Learning Using Very Few Expert Segmentations cover file

Deep Monte-Carlo EM for Semantic Segmentation using Weakly-and-Semi-Supervised Learning Using Very Few Expert Segmentations

2024/06/14
June 2024 issue

Akshay V GaikwadComputer Science and Enginnering, Indian Institute of Technology Bombay, Suyash AwateComputer Science and Enginnering, Indian Institute of Technology Bombay