MIART Workshop 2026
Welcome to the official website of the MIART 2026 workshop, held in conjunction with MICCAI 2026.
At a Glance
- Workshop: Medical Image AI in Radiation Therapy (MIART) at MICCAI 2026
- Date: October 1, 2026
- Duration: Half-day
- Location: TBD
- Format: TBD (In-person/Hybrid)
Announcements
- [May 2026] MIART 2026 has been accepted as an official MICCAI 2026 satellite event. Follow us on LinkedIn for updates.
- [March 2026] The MIART 2026 workshop proposal has been submitted. Further details will be announced in due course.
About the Workshop
Radiotherapy (RT) is a cornerstone of modern oncology, accounting for approximately 40% of curative cancer treatments. RT uses targeted radiation to destroy cancer cells; however, healthy tissues in the radiation path may also be affected. The fundamental challenge is therefore to precisely deliver dose to the tumour while protecting nearby organs. Unlike diagnostic imaging, where the primary goal is detection, RT operates as a complex, multi-stage therapeutic ecosystem requiring the seamless integration of longitudinal imaging, precise anatomical definition, radiation physics, and biological response modelling.
The opacity of deep learning models is a particular concern in RT, where algorithmic decisions directly govern the physical delivery of high-dose radiation. A geometric error in AI-based contouring or a hallucination in image synthesis does not merely result in a misdiagnosis; it can lead to catastrophic geographic misses, reducing the probability of cure and increasing the risk of severe toxicity.
The MIART Workshop aims to establish a dedicated forum within the MICCAI community for researchers applying AI to radiation oncology. The workshop advances a vision of therapeutic AI that is physics-aware and biologically grounded, bridging the gap between data-driven discovery and clinical intervention to support personalised radiotherapy.
Workshop Objectives
- Community Unification – Establish a centralised home within MICCAI for the dispersed radiotherapy AI research community.
- Holistic Optimisation – Advance end-to-end optimisation across the full therapeutic pipeline, moving beyond isolated sub-task solutions.
- Domain Integration – Promote the principled incorporation of physics, biology, and clinical constraints into deep learning models.
- Comprehensive Modelling – Expand predictive AI beyond binary outcomes towards detailed safety, toxicity, and outcome modelling.
Call for Papers
Submissions are invited across the full breadth of the therapeutic AI workflow, including but not limited to:
- Therapeutic Image Synthesis and Reconstruction: Synthetic CT generation, cross-modality translation, and artefact reduction for treatment planning and image guidance.
- Target Definition and Geometric Modelling: Segmentation of tumours and organs-at-risk, uncertainty-aware contouring, and handling of anatomical variability.
- Treatment Optimisation and Automation: AI-driven inverse planning, dose and delivery optimisation, and automated quality assurance, including adaptive radiotherapy and reirradiation.
- Biophysical Modelling and Response Prediction: Integration of biological priors into deep learning for predicting toxicity, tumour control, and patient-specific outcomes.
- Adaptive Therapy and Motion Management: Longitudinal monitoring, real-time tracking, and online adaptation to anatomical changes during treatment.
- Data Scarcity and In Silico Modelling: Digital twins, biomechanical simulation, and synthetic data generation for low-data settings.
- Clinical Translation and Safety: Interpretability, error propagation analysis, and frameworks for safe deployment of AI in high-stakes clinical environments.
- Advanced Modalities: AI for proton beam therapy, MR-guided radiotherapy, and reirradiation.
Submission and Review Process
- Format: Manuscripts should be 8–10 pages (including references), formatted and anonymised according to the Lecture Notes in Computer Science (LNCS) style.
- Review Process: Submissions will undergo double-blind review by three independent reviewers, selected to avoid institutional conflicts of interest.
- Ethics and Privacy: Papers involving patient data must clearly state ethical approval or waiver status. Use of synthetic or simulated data must be explicitly identified.
- Submission System: OpenReview (portal link to be announced).
- Proceedings: Accepted papers will be published under the MICCAI Springer joint publication model.
Important Dates
- Paper Submission Opens: May 2026 (TBD)
- Paper Submission Deadline: July 2026 (TBD)
- Notification of Acceptance: August 2026 (TBD)
- Camera-ready Version: August 2026 (TBD)
- Workshop Date: October 1, 2026
Preliminary Programme
Tentative schedule for a half-day afternoon session.
| Time | Session |
|---|---|
| 14:00 – 14:05 | Opening Session |
| 14:05 – 14:40 | Keynote 1 (AI/Engineering perspective): Invited talk on methodological advances in physics-aware and safety-critical AI for radiotherapy. |
| 14:40 – 15:10 | Accepted Contributions (Methodological depth, grouped thematically) |
| 15:10 – 15:25 | Coffee Break |
| 15:25 – 16:00 | Keynote 2 (Clinician perspective): Invited talk on clinical translation, treatment workflow integration, and patient-centred AI evaluation. |
| 16:00 – 16:30 | Accepted Contributions (Clinical relevance and discussion) |
| 16:30 – 17:10 | Keynote 3 (Medical Physicist perspective): Invited talk on treatment planning, dose modelling, and physics-informed AI. |
| 17:10 – 17:40 | Accepted Contributions (Continued themes and audience discussion) |
| 17:40 – 18:00 | Closing Session and Best Paper Award |
Organising Committee
| Name | Institution | Country | |
|---|---|---|---|
| Mauricio Reyes | University of Bern | Switzerland | mauricio.reyes@med.unibe.ch |
| Oscar Acosta | Université de Rennes | France | oscar.acosta@univ-rennes.fr |
| Javier Pascau | Universidad Carlos III de Madrid | Spain | jpascau@ing.uc3m.es |
| Eliana Vásquez | University of Manchester | United Kingdom | eliana.vasquezosorio@manchester.ac.uk |
| Francesca Spadea | Karlsruhe Institute of Technology | Germany | mf.spadea@kit.edu |
| Gloria Díaz | Instituto Tecnológico Metropolitano | Colombia | gloriadiaz@itm.edu.co |
| Gabor Fichtinger | Queen’s University | Canada | fichting@queensu.ca |
| Parvin Mousavi | Queen’s University | Canada | mousavi@queensu.ca |
| Amith Kamath | University of Bern | Switzerland | amith.kamath@unibe.ch |
Contact
For enquiries, please contact any of the general chairs listed above, or visit the MICCAI 2026 Satellite Events page. Follow us on LinkedIn for the latest updates.
MIART 2026
