Full papers
High-quality, original, and unpublished work submitted in Springer LNCS format, up to 8 pages plus 2 pages of references, through OpenReview with double-blind review.
Machine Learning/CORE Unranked
MLCN 2026
Eighth International Workshop on Machine Learning in Clinical Neuroimaging
MLCN 2026 is an international workshop held in conjunction with MICCAI 2026 in Strasbourg, France, bringing together researchers in machine learning, neuroscience, and clinical neuroimaging to address methodological challenges in analyzing complex neuroimaging data and bridging the translational gap to clinical practice. The workshop focuses on developing stable, scalable, and interpretable machine learning models for clinical applications in brain disorders.
Key deadlines
Verified September 9, 2026Full paper
July 8, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
July 8, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Research areas in scope
01
Machine Learning Track
Big dataSpatio-temporal brain data analysisStructural data analysisGraph theory and complex network analysisLongitudinal data analysisModel stability and interpretabilityModel scalability in large neuroimaging datasetsMulti-source data integration and multi-view learningMulti-site data analysis, from preprocessing to modelingDomain adaptation, data harmonization, and transfer learning in neuroimagingUnsupervised methods for stratifying brain disordersDeep learning in clinical neuroimagingModel uncertainty in clinical predictionsBiomarker discoveryRefinement of nosology and diagnosticsBiological validation of clinical syndromesTreatment outcome predictionCourse predictionAnalysis of wearable sensorsNeurogenetics and brain imaging geneticsMechanistic modelingBrain agingDatabase for machine learning
02
Clinical Neuroimaging Track
Machine learning approaches aimed at improving our understanding of complex brain disordersMoving the field closer to precision medicine
Policies worth checking twice
- Submissions must be high-quality, original, and unpublished.
- Submissions must adhere to Springer Lecture Notes in Computer Science (LNCS) style.
- Submissions must be made through the OpenReview system.
- Double-blind review process is used; submissions must be anonymous.
- Submissions must not exceed 8 pages of content, with an additional 2 pages allowed for references.
- Authors must follow MICCAI 2026 guidelines regarding dual/double submission policy, conflicts of interest, plagiarism, and use of LLMs.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026