Call for Papers: Computer Vision for Microscopy Image Analysis: Large Foundation Models and Explainable Multimodal Analytics (CVMI @ CVPR 2025)

10th IEEE Workshop on Computer Vision for Microscopy Image Analysis (CVMI) will be held in conjunction with CVPR 2025. The emphasis of the 2025 CVMI workshop will be leveraging the advances in large foundational models (LFMs) to improve the multimodal analysis of microscopy and omics data with enhanced explainability.

High-throughput microscopy enables researchers to acquire thousands of images automatically over a matter of hours. This makes it possible to conduct large-scale, image-based experiments for biological discovery. The main challenge and bottleneck in such experiments is the conversion of “big visual data” into interpretable information and hence discoveries. Visual analysis of large-scale image data is a daunting task. Cells need to be located and their phenotype (e.g., shape) described. The behaviors of cell components, cells, or groups of cells need to be analyzed. The cell lineage needs to be traced. Not only do computers have more “stamina” than human annotators for such tasks, they also perform analysis that is more reproducible and less subjective. The post-acquisition component of high-throughput microscopy experiments calls for effective and efficient computer vision techniques.

This workshop intends to draw more visibility and interest to this challenging yet fruitful field, and establish a platform to foster in-depth idea exchange and collaboration. Authors are invited to submit original and innovative papers. We aim for broad scope, topics of interest include but are not limited to: leveraging large foundational models (LFMs) for –

Data: microscopy image generation, annotation, augmentation, enhancement, open-source microscopy image datasets, analysis software and benchmarking.
Analysis: object detection, segmentation, classification, image stitching and registration, object tracking, event detection, shape analysis, texture analysis, 3D analysis, disease classification and prediction, novel machine learning (ML) methods (e.g., domain adaptation, out of distribution capabilities, LFMs) for advanced microscopy analytics, with explainability and interpretability, and integration of ML with domain knowledge, as well as the best strategies and architectures for developing reliable LFMs and mitigating hallucinations.
Multimodal analysis: integration of microscopy with diverse data types including omics, for new knowledge discovery, disease classification and prediction, and to track disease progression and drug response.
Applications of microscopy image analysis: AI-based systems for cancer scoring, systems for integrating omics with imaging for disease understanding and treatment, system design to connect big microscopy image data to knowledge, and finally efficient methods for drug design.

Accepted papers will be included in the CVPR proceedings, on IEEE Xplore, and on CVF website.

Paper Submission Deadline: March 17th 2025, 11:59:59 Pacific Time. Link to submission system: https://cmt3.research.microsoft.com/CVMI2025/Submission/Index

Workshop website: https://cvmi-workshop.github.io/dates.html