About & Announcements
Announcements
- [July 2026] Submission deadline extended! The paper submission deadline has been extended to July 17, 2026 (11:59 PM CEST) — submissions close at 12:00 AM CEST on July 18, 2026. Submit your paper via OpenReview.
- [June 2026] Submissions are now open! Submit your paper via OpenReview.
- [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.