[scikit-learn] Call for papers for the 4th international workshop on machine learning in clinical neuroimaging

[MLCN Workshop]

Dear Colleagues,

*Please find below the call for papers for the International Workshop of
Machine Learning in Clinical Neuroimaging (MLCN) which is held virtually
on the 27th of September 2021 at MICCAI 2021 in Strasbourg, France. We
welcome contributions to novel machine learning methods and their
applications to clinical neuroimaging data.*

The submission deadline is *25 June 2021*, and all MLCN accepted papers
will be eligible for the best paper award of 500 USD. Top accepted papers
will be invited to submit an extended version to the MELBA journal.

For more information, please visit https://mlcnws.com/call-for-papers/*.*

Best wishes,
The MLCN 2021 steering and organizing committees

Christos Davatzikos, Andre Marquand, Jonas Richiardi, Emma Robinson
Ahmed Abdulkadir, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Jane
Maryam Rondina, Chantal Tax, Thomas Wolfers

================================================================================================


Call for Papers

The International Workshop of Machine Learning in Clinical Neuroimaging (
https://mlcnws.com/), a satellite event of MICCAI (https://miccai202
<https://miccai2021.org/>1 <https://miccai2020.org/>.org
<https://miccai2021.org/>), calls for original papers in the field of
clinical neuroimaging data analysis with machine learning. The two tracks
of the workshop include methodological innovations as well as clinical
applications. This highly interdisciplinary topic provides an excellent
platform to connect researchers of varying disciplines and to collectively
advance the field in multiple directions.

In the machine learning track, we seek novel contributions that address
current methodological gaps in analyzing high-dimensional, longitudinal,
and heterogeneous clinical neuroscientific data using stable, scalable, and
interpretable machine learning models. Topics of interest include but are
not limited to:

   - Big data
   - Spatio-temporal brain data analysis
   - Structural data analysis
   - Graph theory and complex network analysis
   - Longitudinal data analysis
   - Model stability and interpretability
   - Model scalability in large neuroimaging datasets
   - Multi-source data integration and multi-view learning
   - Multi-site data analysis, from preprocessing to modeling
   - Domain adaptation, data harmonization, and transfer learning in
   neuroimaging
   - Unsupervised methods for stratifying brain disorders
   - Deep learning in clinical neuroimaging
   - Model uncertainty in clinical predictions
   - …

In the *clinical neuroimaging *track*,* the applications of existing
machine learning algorithms are evaluated to move towards precision
medicine for complex brain disorders. The discovery of biological markers
in medicine is an important challenge across different fields and various
experimental procedures and designs are used to detect biological
signatures that can be utilized for improvement in diagnostic, treatment,
or for other beneficial ends. However, for most complex brain disorders, we
do not have reliable biomarkers today. The application of advanced machine
learning methods may help to reach this goal. Therefore, we invite the
community to submit conference contributions on machine learning approaches
with the goal to improve our understanding of complex brain disorders,
moving the field closer towards precision medicine. Topics of interest
include but are not limited to:

   - Biomarker discovery
   - Refinement of nosology and diagnostics
   - Biological validation of clinical syndromes
   - Treatment outcome prediction
   - Course prediction
   - Analysis of wearable sensors
   - Neurogenetics and brain imaging genetics
   - Mechanistic modeling
   - Brain aging
   - The presentation of clinical neuroimaging databases to stimulate
   developments in machine learning
   - …

------------------------------
Submission Process:

The workshop seeks high-quality, original, and unpublished work that
addresses one or more challenges described above. Papers should be
submitted electronically in Springer Lecture Notes in Computer Science
(LCNS) style (see
https://miccai2021.org/en/PAPER-SUBMISSION-GUIDELINES.html for
detailed author guidelines) using the CMT system at
https://cmt3.research.microsoft.com/MLCN2021. The page limit is 8-pages
(text, figures, and tables) plus up to 2-pages of references. We review the
submissions in a double-blind process. Please make sure that your
submission is anonymous. Accepted papers will be published in a joint
proceeding with the MICCAI 2021 conference.
------------------------------
MLCN Special Issue at the MELBA journal:

This year, we will invite the top accepted papers to submit an extended
version of their contribution to the MLCN special issue at the Journal of
Machine Learning for Biomedical Imaging (MELBA)
<https://www.melba-journal.org/about>. The invited papers will go through
an independent review process by the journal.
------------------------------
Best Paper Award:

All MLCN accepted papers will be eligible for the best paper award. The
recipient of the award will be chosen by the MLCN scientific committee
based on the scientific quality, novelty, and clarity of contributions. The
winner will be announced at the end of the workshop and will receive a 500
USD honorarium.
------------------------------
Important Dates:

   - Paper submission deadline: June 25, 2021, 11:59 PM Pacific Time
   - Notification of Acceptance: July 16, 2021
   - Camera-ready Submission: July 30, 2021
   - Workshop Date: September 27, 2021
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