An official journal of the MICCAI Society

Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. The journal publishes the highest quality, original papers that contribute to the basic science of processing, analysing and utilizing medical and biological images for these purposes. The journal is interested in approaches that utilize biomedical image datasets at all spatial scales, ranging from molecular/cellular imaging to tissue/organ imaging. While not limited to these alone, the typical biomedical image datasets of interest include those acquired from:

    Magnetic resonance
    Ultrasound
    Computed tomography
    Nuclear medicine
    X-ray
    Optical and Confocal Microscopy
    Video and range data images

The types of papers accepted include those that cover the development and implementation of algorithms and strategies based on the use of various models (geometrical, statistical, physical, functional, etc.) to solve the following types of problems, using biomedical image datasets: representation of pictorial data, visualization, feature extraction, segmentation, inter-study and inter-subject registration, longitudinal / temporal studies, image-guided surgery and intervention, texture, shape and motion measurements, spectral analysis, digital anatomical atlases, statistical shape analysis, computational anatomy (modelling normal anatomy and its variations), computational physiology (modelling organs and living systems for image analysis, simulation and training), virtual and augmented reality for therapy planning and guidance, telemedicine with medical images, telepresence in medicine, telesurgery and image-guided medical robots, etc.

Special Issue on Foundation Models for Computational Pathology
提出日: 2025-12-15

The high resolution and complexity of pathology images present unique opportunities for innovation and breakthroughs in foundation model development. With exceptional generalization and transfer capabilities, these models effectively address diverse diagnostic tasks in computational pathology, demonstrating significant clinical potential. Through large-scale data and parameter training, pathology foundation models build universal feature representations, mitigating critical challenges such as data imbalance and heavy annotation dependency. This special issue will focus on innovative research in foundation models for pathology image analysis, covering model development, downstream task applications, clinical research, and real-world deployment strategies. It will emphasize dataset construction, model architecture design, pre-training and fine-tuning methods, as well as the application and translation of models across diverse pathology tasks, driving cutting-edge exploration and practice in pathology AI technology.

Guest editors:

Dr. Shaoting Zhang, Shanghai Artificial Intelligence Laboratory, China
zhangshaoting@pjlab.org.cn

Dr. Fang Yan, Shanghai Artificial Intelligence Laboratory, China
yanfang@pjlab.org.cn

Prof. Ruogu Fang, University of Florida, United States of America
ruogu.fang@bme.ufl.edu

Dr. Xiaoxiao Li, The University of British Columbia, Canada
xiaoxiao.li@ece.ubc.ca

Dr. Herve Delingette, INRIA, France
Herve.Delingette@inria.fr

Prof. Dimitris N. Metaxas, Rutgers University, United States of America
dnm@cs.rutgers.edu

Special issue information:

Topics of interest include but are not limited to:

    Development, validation, and application of unimodal pathology foundation models
    Development, validation, and application of multimodal pathology foundation models
    Disease-specific pathology foundation model development and validation
    Construction of high-quality standardized pathology datasets for model development and validation
    Applications of pathology foundation models in downstream tasks
    Fine-tuning and new algorithm development for pathology foundation models
    Reinforcement learning with human feedback for model optimization and adaptation
    Clinical application and technological development of pathology foundation models in diverse healthcare scenarios
    Explainability research and clinical decision support for pathology foundation models
    Lightweight model design and deployment optimization strategies

Timeline:

    Manuscript Submission Deadline: December 31, 2025
    Initial Review Completion Date: January 31, 2026
    Revised Manuscript Submission Deadline: March 31, 2026
    Review and Revision Process Completion Date (Final Notification): May 31, 2026