COMputational PAthologY and multimodaL data workshop
September 27, 2025

MICCAI 2025

ABOUT

Computational Pathology is a multidisciplinary field encompassing the convergence of Digital Pathology, Medical Image Analysis, Computer Vision, and Machine Learning. The vast amount of information in multi-gigapixel histopathology images positions digital pathology as an ideal use case for advanced image analysis methods. Consequently, deep learning and artificial intelligence have played a pivotal role in advancing computational pathology research in recent years. Furthermore, the integration of pathology data with other medical examinations holds great promise for enhancing diagnostic accuracy and treatment outcomes.

To explore these possibilities with the MICCAI community, after three editions of workshops in Computational Pathology, since 2024, COMPAY has expanded its scope under the new name COMPAYL [kuhm·pile], with a renewed focus on integrating pathology with other data modalities, including language, omics, and radiology, to support precision medicine and translational research.

PREVIOUS EDITIONS

CALL FOR PAPERS

For COMPAYL 2025, we again invite researchers to submit their work in computational pathology in general and in combination with other modalities, such as (but not limited to) language, omics, and radiology. This year, we specifically encourage submissions that focus on spatial biology via spatial transcriptomics or spatial proteomics (named Method of the Year 2024 by Nature) or with a focus on clinical translation and validation of developed applications.

Submissions unrelated to computational pathology or histopathology will be desk-rejected.

Topics of interest include, but are not limited to:

Artificial intelligence and foundation models for multimodal data analysis, including pathology
Integration of digital pathology with genomics, radiology, and textual data
Advances in spatial transcriptomics and whole-slide image analysis
Techniques for stain normalization, tissue structure segmentation, and image registration
Developments in immunohistochemistry scoring, including multiplexing
Innovations in local and cloud-based analysis tools
Research on predictive and prognostic tissue biomarkers
Strategies for integrating and validating computational pathology in clinical workflows

SUBMISSIONS

Authors should prepare a manuscript of no more than 8 pages, including images and tables (with possible extra pages containing only cited references). Supplementary material can be added to the paper, but reviewers are not expected to read it. The formatting guidelines are the same as for the Journal of Machine Learning Research (JMLR). Please ensure your submissions comply with the provided guidelines.

Manuscripts can be submitted online through the OpenReview system.

Please note that new profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.

OVERLEAF TEMPLATE

SUBMIT PAPER

All submissions will be peer-reviewed (single-blind).

All accepted papers will be published on Proceedings of Machine Learning Research (PMLR).

Do not hesitate to contact us if you have any questions about your submission or its scope.