Dr. Camila González
Assistant Professor
Medical University of Vienna, Austria
THE 3RD MICCAI STUDENT BOARD (MSB) EMERGE WORKSHOP
📍 MSB EMERGE 26 will be held in-person on September 27, 2026 as a half-day satellite event of MICCAI 2026 in Strasbourg, France 🇫🇷
The MICCAI Student Board EMERGE Workshop, now in its third edition, is a platform dedicated to empowering early-career, student-led research within the MICCAI community. Building on the success of its inaugural year, EMERGE offers early-career researchers a unique opportunity to present and discuss their work, engage with senior researchers, and foster new collaborations in a mentorship-oriented and supportive setting.
Organized by students, for students, the workshop encourages independent research by supporting young investigators in their scientific and professional development. EMERGE provides an inclusive and supportive environment for students at all levels—undergraduate, master's, and doctoral—as well as recent graduates and early-career researchers to showcase their contributions to medical image computing and computer-assisted interventions. Presentations are designed to be mentorship- and constructive feedback–oriented, designed to provide constructive feedback from senior experts. The program includes keynotes by successful early-career researchers and career development talks tailored to young scientists.
The scope of the workshop covers medical image computing (MIC) and computer-assisted interventions (CAI), in line with the scientific goals of the MICCAI Society. We welcome original research that introduces new methods, applies existing techniques in novel ways, or provides in-depth analysis of medical imaging problems. Contributions may also include rigorous benchmarking or the release of new datasets that support the development and evaluation of medical imaging approaches.
This includes work on image formation, reconstruction, segmentation, diagnosis, and decision support. Studies combining imaging with clinical, molecular, or wearable data, or handling multimodal and longitudinal inputs, are also welcome. We encourage submissions focused on building reliable, fair, and generalizable AI systems, as well as solutions for personalized imaging, point-of-care use, and underserved populations. Topics such as surgical data science, robotic interventions, and teleradiology are of interest. Research targeting region-specific challenges—especially in the Pan-Asian context—is welcome and reflects this year’s special emphasis on the region. With growing interest in emerging areas such as foundation models and generative AI, we also invite submissions exploring their applications in medical imaging and computer-assisted interventions.
All dates are in 23:59 Anywhere on Earth (AoE)
| Call for papers | April 7, 2026 |
| Submission portal opens | May 15, 2026 |
| Paper submission due | July 10, 2026 |
| Reviews released to authors | July 23, 2026 |
| Rebuttals due | July 29, 2026 |
| Final decisions | August 3, 2026 |
| Camera-ready due | August 12, 2026 |
| Workshop day | September 27, 2026 |
Submissions will undergo double-blind peer review and must follow these guidelines: a maximum of 8 pages for content (including figures and tables), with up to 2 additional pages for references. The paper template and formatting details can be found here. Submissions should follow the same guidelines and process as the MICCAI 2026 main conference.
Manuscripts must be submitted via the MSB EMERGE 2026 OpenReview submission portal.
Each submission will be reviewed by at least three reviewers. For accepted papers, official reviews are anonymous and publicly visible. Rebuttals and preprints will also be made publicly available on the OpenReview platform.
Assistant Professor
Medical University of Vienna, Austria
All times are local time in Strasbourg, France (CEST), on September 27, 2026.
| Time | Session |
|---|---|
| 13:30 - 13:40 | Opening Remarks and Introduction |
| 13:40 - 14:05 | Poster Teasers (20 × 1 min) |
| 14:05 - 14:40 |
Oral Session 1: Multimodal & Generative Learning
|
| 14:40 - 15:15 |
Oral Session 2: Trustworthy & Interpretable AI
|
| 15:15 - 16:05 | Coffee + Posters |
| 16:05 - 16:40 | Keynote — Dr. Camila González, Medical University of Vienna, Austria |
| 16:40 - 17:25 |
Oral Session 3: Label-Efficient & Weakly Supervised Segmentation
|
| 17:25 - 17:50 |
Oral Session 4: Conference-to-Workshop Track
|
| 17:50 - 18:00 | Closing Remarks and Awards |
| # | Title |
|---|---|
| 4 | Fully Automated CT-Based Differential Diagnosis of Bowel Wall Thickening |
| 6 | Multi-Caption Guided Weakly Supervised Disease Localization on Medical Cancer Images |
| 7 | When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI |
| 9 | FadeFormer: Content-Adaptive Graph Diffusion for Medical Image Classification |
| 10 | Replication and Reproducibility Analysis of Breast Tumor Classification and Segmentation Benchmarks on the BreastDM Dataset |
| 13 | Multi-Paradigm Fusion of XAI Methods for Chest X-ray Explanations |
| 14 | Label- and Parameter-Efficient Lung Ultrasound Representation Learning via I-JEPA for Edge Diagnostics |
| 18 | When Simple Wins: Lightweight CNN Encoders for Resource-Constrained Nucleus Segmentation |
| 19 | Feature Extraction Strategies for Clinically Significant Prostate Cancer Detection on Biparametric MRI: A Systematic Ablation of the Local-Global MIL Framework |
| 20 | Evaluating Concept-guided Visual Counterfactual Generation in Medical Imaging |
| 21 | KneeDINO: Cross-Plane Attention Fusion for Multi-Label Knee Pathology Detection with Grounded LLM Explainability |
| 25 | GraM-Diff: A Unified Graph–Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis |
| 27 | GPT-DBR: Decoding-Based Language-Model Regression for CT-Derived Lung-Function Estimation |
| 28 | Linear and Non-Linear Dimensionality Reduction for Hyperspectral Overlapping Chromosome Segmentation |
| 31 | Relational Learning of Temporal and Semantic Clinical Structured EHRs for Enhanced Patient Outcome Forecasting |
| 36 | Annotation-Free Structured Delineation for Weakly Supervised PET Lesion Segmentation |
| 37 | MedHyperGraph: EHR-Integrated Multimodal Hyperedges for Clinical VQA |
| 38 | LocSAM3: Box-Supervised Adaptation of SAM3 for Text-Only Chest X-Ray Segmentation |
| 39 | Label-Efficient Multimodal Microsleep Forecasting via Interpretable Disagreement-Driven Active Learning |
| 40 | GF-BrainSR: Gated Frequency-Aware Selective State Space Model-Based Brain MRI Image Super-resolution |







President, MICCAI Student Board
IIIT Hyderabad, India
Vice-President, MICCAI Student Board
Technical University of Munich, Germany
Scientific Events Officer, MICCAI Student Board
University of Notre Dame, United States
Assistant Professor
Medical University of Vienna, Austria
Research Scientist
MBZUAI, United Arab Emirates
Research Scientist
Harvard Medical School, United States
Distinguished Professor
Shenzhen University, China
Assistant Professor
University of Chicago, United States