Call for Papers

We would like to invite you to submit your papers for the Efficient Medical AI 2025 Workshop to be held in September 2025 in Daejeon, Republic of Korea, as a satellite workshop of MICCAI.

The EMA4MICCAI Workshop seeks to bridge the gap between computational efficiency and cutting-edge AI methodologies within the medical domain. While much progress has been made in developing high-performance AI models, their real-world adoption in clinical workflows is often hindered by inefficiencies in computation and resource requirements. EMA4MICCAI aims to bring together researchers, engineers, and clinicians to explore solutions that make medical AI more lightweight, energy-efficient, and deployable at scale.

The conference program will include paper presentations, a poster session and keynote talks by prominent speakers in the field. All submitted papers will be reviewed by at least three PC members and handled by an Area Chair. Acceptance decisions will take into account paper novelty, technical depth, practical or theoretical impact. All accepted papers will be published in LNCS proceedings.

Where to Submit

Scope

We invite topics related to medical imaging and efficient medical modeling, which include but are not limited to:

• Lightweight Architectures for Real-Time Medical Imaging AI

  • • Neural architecture search (NAS)

    • Knowledge distillation

    • Pruning and quantization techniques

    • Low-rank approximation methods

    • Model compression

• Scalable Federated Learning for Privacy-Preserving Healthcare AI

  • • clinical data collection & analysis pipelines

    • multi-modal and/or longitudinal data management & analysis

    • data selection techniques

    • data bias analysis

    • annotation tools and strategies

• High-Performance AI for Resource-Constrained Clinical Environments

  • • domain adaptation/generalization

    • federated learning

    • few-shot/zero-shot learning

    • Semi-/self-/un-/weakly-supervised learning

    • active learning

    • multi-modal learning

    • noisy & incomplete data

• Real-Time Decision Support Systems Using Low-Latency AI Models

  • • Test-time adaptation

    • Scalable systems for under-resourced hospitals or clinics

    • Emerging Hardware Solutions for Efficient Medical AI Computing

• Efficient Generative Models for Medical Image Synthesis and Augmentation

  • • generative modeling

    • neural rendering

    • style transfer

    • physics-based simulation

    • sim2real gap

    • label augmentation

Important Dates

Submission Instruction