Donders Talent Award (DTA) 2021

We are glad to announce the call for MLCN2021’s ‘Donders Talent Award’ (DTA). DTA is sponsored by Donders Institute and aims at supporting talented students from developing countries with interest and proven talent in the area of machine learning in clinical neuroimaging. The Donders Institute is a research centre devoted to understanding human cognition and behavior in health and disease. Hundreds of international researchers aim at the advancement of brain-, cognitive and behavioral science and improving health, education, and technology.

DTA covers the MLCN2021 workshop registration fee that is held this year in a virtual format. Eligible candidates are:

  • Bachelor, Master, or Ph.D. students studying in developing countries (see here for the list of eligible countries)
  • Proven record of study and research in the machine learning in clinical neuroimaging domain

The application form is available at this link. The applications that are submitted before the 20th of August 2021 will be considered and evaluated by the MLCN 2021 organizing committee.

For more information and questions please contact Dr. Vinod Kumar (vinod.kumar@tuebingen.mpg.de) or Dr. Thomas Wolfers (thomas.wolfers@donders.ru.nl).

Donders Institute will sponsor MLCN 2021

We are delighted to announce that the MLCN 2021 will be sponsored by Donders Institute. The Donders Institute is a research centre devoted to understanding human cognition and behavior in health and disease. Hundreds of international researchers aim at the advancement of brain, cognitive and behavioral science and improving health, education and technology.

According to our agreement, Donders Institute will generously cover i) the MLCN2021’s best paper award, Donders best paper award (see here for more details); ii) registration costs of our invited keynote speakers (see here for more details); and iii) registration fees for talented students from developing countries, Donders talent award (you can apply here).

MLCN 2021 Call for Sponsors

The International Workshop of Machine Learning in Clinical Neuroimaging, a satellite event of MICCAI (https://miccai2021.org). It is a highly interdisciplinary workshop which provides a forum for state-of-art developments and applications in machine learning, brain imaging, genetics for researchers in those fields! We have organized this workshop for the last three years. Each year it has been growing substantially. We approach possible partners and sponsors to help us developing this workshop further. Therefore, we introduced three partnership programs below. While we would welcome a contribution within these partnerships, any kind of support would be a delight to us. The contribution of the sponsors will be acknowledged on the workshop website and during the event.

We will inform our sponsors after the event on how we spent the money so that it is transparent what our sponsors contributed to. We would be extremely grateful over sponsorship and use it with the following priority.

  • best paper(s) award.
  • conference fees for invited speakers.
  • support of students from developing countries to attend our workshop (if we have funds).

Golden Sponsorship 1954$: ENIAC Women (1954) Partnership

This partnership is named in honor of ladies who programmed the first computer: Fran Bilas, Jean Bartik, Ruth Lichterman, Kay McNulty, Betty Snyder, and Marlyn Wescoff.

 Silver Sponsorship 780$: al-Khwarizmi (780–850) Partnership

Al-Khwarizmi was the first to treat algebra as an independent discipline and therefore he has been described as the father or founder of algebra. Also, the word ‘algorithm’ has its roots in Latinizing his name.


Bronze Sponsorship 400$: Kharosthi (400 BC) Partnership

Kharosthi the first script that introduced decimals invented in India then transmitted to the Middle East, China and Europe.

For more information please contact Dr. Thomas Wolfers (thomas<dot>wolfers<at>psykologi<dot>uio<dot>no).

The MLCN2020 Best Paper Award

Based on the evaluation of the MLCN 2020 scientific committee members on the scientific content, significance of the contribution, and clarity of the communication, the MLCN2020 best paper award is presented to Naresh Nandakumar and his coauthors Niharika  S. D’Souza, Komal Manzoor, Jay Pillai, Sachin Gujar, Haris Sair, and Archana Venkataraman for the paper entitled “A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity”.

Naresh Nandakumar

Naresh is currently a 4th year PhD student in the Neural Systems Analysis lab at the Johns Hopkins University. He received his Bachelor’s from Vanderbilt University, where he did research in the MASI lab. His research interests lie at the intersection of statistical modeling/deep learning and clinical neuroimaging. He has presented at ISBI 2018, MICCAI 2018, MICCAI 2019 and MICCAI 2020. He develops machine learning models to tackle clinically relevant problems. Currently, he is working on eloquent cortex detection of brain tumor patients using resting-state fMRI connectivity. He is always eager to speak with prospective collaborators, you can reach him at nnandak1@jhu.edu or on his LinkedIn

The winner is selected among 18 accepted papers for the presentation at the MLCN 2020. The runner-up papers include:

  • Jianyuan Zhang, Feng Shi, Lei Chen, Zhong Xue, Lichi Zhang, and Dahong Qian: “Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation Learning
  • Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, and Nils D. Forkert: “Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows
  • Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz: “Surface Agnostic Metrics for Cortical Volume Segmentation and Regression
  • Yannick Suter, Urspeter Knecht, Roland Wiest, Ekkehard Hewer, Philippe Schucht, and Mauricio Reyes: “Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning