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IPMI 2027: International Conference on Information Processing in Medical Imaging

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Submission Date:
2026-12-07 Due in 92 days
Notification Date:
2027-02-15
Conference Date:
2027-06-27
Location:
Lake Stukely, Quebec, Canada
Years:
Viewed: 32837   Tracked: 8   Attend: 1

Conference Partner Index (CP-I)

58.5 / 100
Ranked #633 of 5,650 conferences · Top 12%

#12 of 182 in Bioinformatics & Health Informatics #21 of 241 in Computer Vision & Pattern Recognition

Academic recognition (35%) No data - scored at the neutral baseline of 50
Submission selectivity (20%) No data - scored at the neutral baseline of 50
Editions held (20%)
96
Community attention (10%)
36
Public record completeness (15%)
55

Inputs used: Editions on record: 34 · Researchers following it here: 8 · Researchers who opened this page in the past 24 months: 6

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-05

Call For Papers

IPMI 2027 (International Conference on Information Processing in Medical Imaging) is an academic conference held in Lake Stukely, Quebec, Canada on 2027-06-27. The paper submission deadline is 2026-12-07. Acceptance notifications are sent on 2027-02-15.

The Topics Following topics, and beyond, are welcome: ● Mathematical Foundations and Computational Methods Inverse problems and optimization theory • Computational anatomy and physiology • Geometric learning • Statistical and computational modeling • Quantum computing applications ● Image Formation and Reconstruction Image acquisition and reconstruction methods • Image registration, segmentation and fusion • Functional, diffusion and molecular imaging • Multidimensional, multimodality, multiscale problems ● Machine Learning and Intelligence Novel deep learning architectures and theory • Foundation models and multimodal learning • Generative modeling and synthesis • Transfer learning, domain adaptation and federated approaches • Uncertainty quantification and robust learning • Interpretable and causal learning methods ● Clinical Applications and Translation Image-guided interventions and surgery • Computer-aided diagnosis and treatment • Imaging genomics and omics integration • Digital twins and augmented reality applications ● Visualization and Human-AI Interaction Advanced visualization and immersive interfaces • Privacy-preserving and sustainable computing
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