NEWS!
- 04.03.2025: Excellent News - our proposal for GRAIL 2025 was accepted as an in-person workshop at MICCAI 2025. Stay tuned for further announcements!
GRAIL 2025 is the seventh international Workshop on GRaphs in biomedicAl Image anaLysis, organised as an in-person satellite event of MICCAI 2025 in Daejeon, Korea.
Graphs provide a flexible and scalable mathematical framework to model complex, unstructured data in an interpretable manner. They serve as a foundation for advanced computational models, enabling key techniques such as spectral analysis, dimensionality reduction, and network analysis. Since 2017, geometric deep learning has revolutionized the field by integrating graph signal processing with deep neural architectures, driving innovation in various medical domains. Today, GNNs are widely applied in medical imaging, shape analysis, brain connectomics, population models, patient multi-omics, and drug discovery. Their impact has grown significantly, with increasing visibility at leading machine learning and computer vision conferences such as CVPR, ICLR, and NeurIPS, where they have emerged as a dominant research area in recent years. GRAIL aims to bridge the gap between theory and application, creating a space where scientists developing graph-based models can collaborate with researchers tackling complex clinical challenges across diverse biomedical datasets. GRAIL 2023 featured keynote talks from leading experts and showcased groundbreaking research in brain connectomics, whole-slide image analytics, biomedical knowledge graphs, explainable AI for GNNs, and multi-omics patient representations. GRAIL 2024 continued this momentum with contributions spanning multimodal fusion in GNNs, topological deep learning for drug discovery, disease classification in ophthalmology and Parkinson's disease, and histopathological graph-based analysis. For GRAIL 2025, we aim to expand the workshop's reach and impact by introducing several new initiatives. Our GRAIL Journal Club, launched in 2024, will continue with regular sessions throughout the year, fostering ongoing discussions beyond the MICCAI event. We are also extending our "Getting Started with GNNs" initiative, providing curated resources—including tutorials, code repositories (e.g., PyG/DGL), multimodal datasets, and highlighted papers—to support newcomers and experts alike. Additionally, we are launching a Spotlight Track for MICCAI main conference papers, allowing authors to present short abstracts and single-slide spotlights to enhance visibility and cross-pollination between conference and workshop attendees. To ensure broader accessibility, we will video-record all sessions and improve AV quality for publishing on a dedicated GRAIL YouTube channel. This year, we further broaden our scope to include Topological Deep Learning (TDL), Knowledge Graphs (KGs), Foundation Models (FMs), and the integration of Large Language Models (LLMs) with graphs/GNNs/KGs, particularly in areas such as retrieval-augmented generation (RAG) for biomedical applications. Through these efforts, GRAIL 2025 aims to remain at the forefront of graph-based research in biomedical image analysis, providing a dynamic and inclusive platform for advancing this rapidly evolving field.
As GRAIL 2025 will be an in-person workshop, registration will happen via the main conference website.
Welcome to the GRAIL Workshop Journal Club! Our mission is to create a vibrant and engaging community where we read and discuss the latest cutting-edge papers, bringing the Graph Deep Learning (GDL) community closer together. Join us monthly to foster collaborations, brainstorm innovative ideas, and connect with fellow GDL researchers. Whether you're looking to stay updated on the newest advancements or seeking a platform to share your insights or discuss your research and gain feedback, the GRAIL Workshop Journal Club is the perfect place for you. Let's advance the frontiers of GDL together! If you cannot access the form and want to be part of the journal club, please send an email to akazi1@mgh.harvard.edu and niharika.dsouza@ibm.com
Registration: Register here
Next Meeting: Kindly sign up to receive the most up-to-date information.
Meeting platform: Via Zoom