MIDL 2020 special issue
We are pleased to announce the first MELBA special issue on selected papers from the Medical Imaging with Deep Learning conference held virtually, also for the first time, in Montreal from July 6 to 9, 2020. The annual MIDL conference attracts world-class researchers, engineers, as well as clinicians, who develop novel algorithms to solve medical imaging problems using deep learning. The virtual conference had 3,575 registered participants. Two submission tracks were received through the OpenReview system, 148 full papers and 106 short papers. The conference selection had 65 full papers and 40 short papers. A smaller subset of 20 papers were invited to submit an extended journal version to this MELBA special issue, based on careful examination of the submission, AC, reviewer and rebuttal comments. These submissions have gone through a new thorough review process.
The final selection of 8 papers covers various topics, including learning under uncertainty, image generation and reconstruction, now presented in this MELBA special issue.
- Marleen de Bruijne, University of Copenhagen and Erasmus MC;
- Tal Arbel, McGill University;
- Ismail Ben Ayed, ÉTS Montréal;
- Hervé Lombaert, ÉTS Montréal.
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
2021/09/07
Richard ShawUniversity College London, UK, Carole H. SudreKing’s College London, UK, Sebastien OurselinKing’s College London, UK, M. Jorge CardosoKing’s College London, UK, Hugh G. PembertonUniversity College London, UK
PDF Video
Adversarial Robust Training of Deep Learning MRI Reconstruction Models
2021/04/28
Francesco CaliváCenter for Intelligent Imaging (CI2), University of California, San Francisco, Kaiyang ChengCenter for Intelligent Imaging (CI2), University of California, San Francisco
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Rutwik ShahCenter for Intelligent Imaging (CI2), University of California, San Francisco, Valentina PedoiaCenter for Intelligent Imaging (CI2), University of California, San Francisco
Recalibration of Aleatoric and EpistemicRegression Uncertainty in Medical Imaging
2021/04/28
Max-Heinrich LavesInstitute of Medical Technology and Intelligent Systems, Hamburg University of Technology
Institute of Mechatronic Systems, Leibniz Universit ̈at Hannover, Sontje IhlerInstitute of Mechatronic Systems, Leibniz Universit ̈at Hannover, Jacob F. FastInstitute of Mechatronic Systems, Leibniz Universit ̈at Hannover
Hannover Medical School, Lüder A. KahrsCentre for Image Guided Innovation and Therapeutic Intervention, The Hospital for Sick Children, Toronto
Department of Mathematical and Computational Sciences, University of Toronto Mississauga, Tobias OrtmaierInstitute of Mechatronic Systems, Leibniz Universit ̈at Hannover
Locally orderless tensor networks for classifying two- and three-dimensional medical images
2021/03/23
Raghavendra SelvanDepartment of Computer Science, University of Copenhagen, Denmark
Department of Neuroscience, University of Copenhagen, Denmark, Silas ØrtingDepartment of Computer Science, University of Copenhagen, Denmark, Erik B DamDepartment of Computer Science, University of Copenhagen, Denmark
Probabilistic dipole inversion for adaptive quantitative susceptibility mapping
2021/03/12
Jinwei Zhang Cornell University, Ithaca
Weill Medical College of Cornell University, New York, Hang Zhang Cornell University, Ithaca
Weill Medical College of Cornell University, New York, Mert Sabuncu Cornell University, Ithaca
Weill Medical College of Cornell University, New York, Pascal SpincemailleWeill Medical College of Cornell University, New York, Thanh NguyenWeill Medical College of Cornell University, New York, Yi WangCornell University, Ithac
Weill Medical College of Cornell University, New York
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
2020/12/11
Roger David Soberanis MukulTechnical University of Munich, Nassir NavabTechnical University of Munich
Johns Hopkins University, Baltimore, Shadi AlbarqouniTechnical University of Munich
Helmholtz AI, Helmholtz Center Munich