MLCN 2020: Machine Learning in Clinical Neuroimaging (Lima, Peru, Virtual)
The 3rd international workshop on machine learning in clinical neuroimaging (MLCN 2020) was held in conjunction with MICCAI 2020 aimed to bring together experts in both machine learning and clinical neuroimaging to address two main challenges in the field: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data; and 2) filling the translational gap in applying existing machine learning methods in clinical practices.
The call for papers for the MLCN 2020 workshop was released on May 8, 2020, with the manuscript submission deadline set to July 10, 2020. The received manuscripts went through a double-blind review process by MLCN 2020 program committee members. Each paper is thoroughly reviewed by at least three reviewers and the top 18 papers were qualified for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging.
Organising Committee: Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Seyed Mostafa Kia, Jane Maryam Rondina, Chantal Tax, Hongzhi Wang, Thomas Wolfers
Steering Committee: Christos Davatzikos, Andre Marquand, Jonas Richiardi, Emma Robinson
MLCN 2019: Entering the Era of Big Data via Transfer Learning and Data Harmonization (Shenzhen, China)
The 2nd international workshop on Machine Learning in Clinical Neuroimaging (MLCN 2019) was held in conjunction with MICCAI 2019, with a special focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience.
The call for papers for the MLCN 2019 workshop was released on April 14, 2019, with the manuscript submission deadline set to July 14, 2019. The received manuscripts went through a double-blind review process by MLCN 2019 program committee members. Each paper is thoroughly reviewed by at least four reviewers and the top six papers were qualified for publication. The accepted contributions addressed the application of machine learning to generally small sample size neuroimaging data through novel methodologies for data harmonization and transfer learning.
Organising Committee: Mohamad Habes, Seyed Mostafa Kia, Tommy Löfstedt, Kerstin Ritter, Hongzhi Wang
Steering Committee: Christos Davatzikos, Edouard Duchesnay, Andre Marquand, Emma Robinson
MLCN 2018: Spatially Structured Data Analysis (Granada, Spain)
The first international workshop on Machine Learning in Clinical Neuroimaging (MLCN) is held with MICCAI 2018, with a special focus on spatially structured data analysis. The workshop aimed to bring together top-notch researchers in machine learning and clinical neuroscience to discuss and hopefully bridge the existing gap in applied machine learning in clinical neuroscience. The main objective is to shed light on the opportunities and challenges in the structure-aware modeling of neuroimaging data in both encoding and decoding settings. For the keynote talks, we invited leading researchers in the domain of spatial statistics, pattern recognition in neuroimaging, and predictive clinical neuroscience Prof. Christos Davatzikos (University of Pennsylvania), Dr. Gael Varoquaux (INRIA), and Dr. George Langs (Medical University of Vienna) in order to provide a comprehensive overview from theory to application in the field.
The call for papers for the MLCN 2018 workshop was released on April 1, 2018, with the paper deadline set to July 25, 2018. The received manuscript went through a rigorous review process by MLCN scientific committee (Ehsan Adeli, Andre Altman, Luca Ambrogioni, Richard Dinga, Koen Haak, Christina Isakoglou, Emanuele Olivetti, Pradeep Reddy Raamana, Kerstin Ritter, Sourena Soheili Nezhad, Thomas Wolfers, and Maryam Zabihi), and the top four papers with the best reviews were accepted for publication within this proceedings. The accepted contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer’s disease diagnosis and multi-site neuroimaging data analysis.
Organising Committee: Seyed Mostafa Kia, Andre Marquand, Edouard Duchesnay, Tommy Löfstedt.