To the ELLIS Board,

We, the undersigned ELLIS and/or MICCAI Fellows, Scholars, and Members, are writing to propose the inclusion of the Medical Image Computing and Computer Assisted Intervention (MICCAI, https://conferences.miccai.org) Conference Series as a recognised publication venue within the ELLIS ecosystem.

ELLIS has successfully established itself as a beacon of excellence in European AI. As members of this community, we are eager to ensure that the ELLIS Health and other Programmes with impact in medicine, healthcare, biology and biomedicine are fully supported by recognising the premier venues where their impactful research is published. We believe that incorporating MICCAI would be a strategic enhancement to the current list of venues, better reflecting the activities of the European translational AI community and thus contributing to their goals and recognition.

We present the following points to highlight how MICCAI aligns with ELLIS’s standards of excellence and strategic priorities:

1. Alignment in Scientific Rigour and Excellence

A core pillar of ELLIS is the maintenance of high scientific standards. MICCAI shares this commitment to rigour, operating with a selectivity and integrity comparable to currently recognised venues such as NeurIPS, ICML, and CVPR (cf. Appendix 1), while uniquely embedding these contributions in a clinically meaningful context. 

Selectivity: MICCAI maintains a highly competitive acceptance rate in 2020-2025, consistently falling below 30-33% (with 9-14% for early acceptance and lower for oral presentations).

Integrity: The conference employs a rigorous double-blind peer-review process, prioritising methodologically sound and innovative contributions.

This excellence is the reason this Conference, which started in 1998, has been organised on five continents so far and continues to grow. It received over 3,400 submissions in 2025 and accepted 1,027 papers. 

2. Strengthening the ELLIS Health and Other Programmes

The ELLIS Health and other Programmes with direct implications for medicine and biology are vital initiatives. However, ELLIS's impact on medicine and biology manifests across many of its Programmes, bringing together scientists with primary or secondary interests in health, biology, medical robotics, or drug discovery, to name a few. MICCAI serves as a premier venue for medical and biomedical applications (cf. Appendix 2) and a meeting point for many researchers interested in AI for computational medical imaging (cf. Appendix 3). Recognising MICCAI would align ELLIS's metrics with the primary publication habits of many experts in its Health community. This alignment would further encourage participation and cohesiveness among members working at the intersection of AI and healthcare, several of whom also serve as Area Chairs for core conferences such as NeurIPS, CVPR, ICML, ECCV, EurIPS, etc. An additional important aspect is the evaluation and career progression of ELLIS members, especially those with interdisciplinary backgrounds. Currently, these researchers do not fit neatly into a single, specific team due to their interdisciplinarity. Recognising MICCAI helps fill this gap, allowing these researchers to showcase their work and gain recognition that will enhance their careers.

3. Bridging Methodology and Clinical Utility

MICCAI receives submissions covering methodological contributions across virtually all ELLIS areas: deep learning, representation learning, natural language processing, foundation and large-scale models, self-supervised and weakly supervised learning, probabilistic and Bayesian methods, causal inference, multimodal learning, uncertainty quantification, robustness and generalisation, as well as evaluation methodology and benchmarking. Regarding evaluation and reproducibility, MICCAI led to the creation of Grand Challenges (https://grand-challenge.org), which provide clinically grounded datasets and assessment best practices (cf. Appendix 4).

MICCAI offers a unique value by calling for contributions that connect methodological progress with clinical practice. This dual focus aligns well with the ELLIS goal of "AI for Social Good." Recognising MICCAI affirms the importance of translational research—where AI theory meets real-world medical data to address urgent health issues. Tackling practical needs, such as data governance, is crucial in medicine; MICCAI's connections with leading clinical societies (e.g., European Society of Radiology, American College of Radiology, etc.) will broaden the reach and impact of ELLIS's foundational work and support its commitments to trustworthy, responsible, and human-centric AI.

4. Enhancing European Industry and Economic Impact

ELLIS aims to foster robust industry collaboration. MICCAI is a key gathering point for R&D leaders from major European and global players, including Siemens Healthineers, Philips, GE Healthcare, NVIDIA, Microsoft and Google Health. Formal recognition of this venue would strengthen the bridge between ELLIS academia and the medical technology industry, facilitating greater economic impact and partnership opportunities. For instance, EurIPS (https://eurips.cc had a Startup Village that was heavily inspired by similar activities at the recent MICCAI Conference.

5. Opportunities for Co-Educational Synergy 

Both ELLIS and MICCAI share a rich tradition of fostering talent through comprehensive workshops, tutorials, and benchmark challenges. Formal recognition would pave the way for joint educational initiatives that leverage these complementary strengths. This intellectual alignment is already evident, as multiple ELLIS Fellows have featured prominently as Keynote speakers, Satellite Event organisers or contributors at MICCAI conferences, further illustrating the strong scientific bonds that already exist between our communities.

Conclusion

We believe that the inclusion of MICCAI would significantly benefit the cohesion of the ELLIS network and better represent the diverse excellence of the European AI landscape. We respectfully invite the ELLIS leadership to review MICCAI’s standing and consider it for formal recognition as a top-tier venue.

We look forward to working together to continue building a truly inclusive home for European AI excellence.

Sincerely,

Prof Alejandro F Frangi FREng, University of Manchester, ELLIS Fellow, MICCAI Fellow, IEEE Fellow

Dr Claudia Lindner, University of Manchester, ELLIS Member

On behalf of all signatories. 

____________________________

APPENDIX 1:

Key features of the MICCAI Conference Review Process

1) Responsibilities

• Program Chairs (decision)
• Main Area Chair (based on TPMS, paper handling)
• Two additional Area Chairs (based on TPMS recommendations)
• Three or more reviewers per paper (based on bidding process)

2) Double-blind Review Phase (6 weeks)

• 7-point reviewing scale (0 out of scope, 1 Strong reject …. 6 strong accept). From 2026 onwards, the system will change to a 6-point scale (removing 0, the out-of-scope category).
• AC monitors the quality of submitted reviews and asks reviewers for more detailed assessments
• ~48% directly rejected, ~10% “early accepted”, ~42% go into rebuttal period based on 2025 figures

3) Rebuttal period (one week)

• Revision of initial reviews
• Assignment of all rebuttal papers to 3 ACs (original + 2 new ACs based on TPMS)
• Independent recommendation for acceptance or rejection of all 3 ACs

4) Final decision by the PCs based on 3 AC recommendations and 3 reviews

APPENDIX 2:

MICCAI Conference Series esteem indicators

• GoogleScholar, including journals, MICCAI ranking #4 venue in medical imaging 
• ICORE: MICCAI considered “A” and thus top 15%
• Research.com MICCAI rank #1 Conference for Biomedical and Medical Engineering

APPENDIX 3:

ELLIS & MICCAI, and Beyond: Mapping the Overlap

According to the best estimates from the ELLIS and MICCAI websites, approximately 480 ELLIS Fellows and Scholars exist, at least 45 of whom are members of the Health or Molecular Discovery Programmes or working on healthcare applications. While MICCAI is not necessarily the regular society for all of them, it is the natural choice for at least half of these colleagues: Joint MICCAI/ELLIS Fellows include van Ginneken, Frangi, Isgum, L Maier-Hein, Niessen, Rueckert, Schnabel, and Tsaftaris, and ELLIS Scholars or senior Members like Cheplygina, Lekadir, Radeva and Wachinger are regular MICCAI attendees. Key ELLIS fellows such as Ananiadou, A Bronstein, M Bronstein, Colwell, Escalera, Feragen, K Maier-Hein, Lawrence, Murino, Rätsch, C Sanchez, van der Schaar, Welling, and Zeggini have a primary or key interest in health, biology, or chemistry.

APPENDIX 4:

Contributions to enhance benchmarking, reproducibility, and validation with MICCAI (and ELLIS) member's involvement

1. Reinke et al. Advancing standards in biomedical image analysis validation: A perspective on Metrics Reloaded. Clin Transl Med. 2025 Sep;15(9):e70237. doi:10.1002/ctm2.70237.
2. Collins et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation. Ewha Med J. 2025 Jul;48(3):e48. doi:10.12771/emj.2025.00668.
3. Godau et al. Navigating prevalence shifts in image analysis algorithm deployment. Med Image Anal. 2025 May;102:103504. doi:10.1016/j.media.2025.103504.
4. Moons et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025 Mar 24;388:e082505. doi:10.1136/bmj-2024-082505.
5. Lekadir et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025 Feb 5;388:e081554. doi:10.1136/bmj-2024-081554.
6. Reinke et al. Understanding metric-related pitfalls in image analysis validation. Nat Methods. 2024 Feb;21(2):182-194. doi:10.1038/s41592-023-02150-0.
7. Maier-Hein et al. Metrics reloaded: recommendations for image analysis validation. Nat Methods. 2024 Feb;21(2):195-212. doi:10.1038/s41592-023-02151-z.
8. Reinke et al. Understanding metric-related pitfalls in image analysis validation. ArXiv [Preprint]. 2024 Feb 23:arXiv:2302.01790v4. doi:10.1038/s41592-023-02150-0.
9. Roß et al. Beyond rankings: Learning (more) from algorithm validation. Med Image Anal. 2023 May;86:102765. doi:10.1016/j.media.2023.102765.
10. Antonelli et al. The Medical Segmentation Decathlon. Nat Commun. 2022 Jul 15;13(1):4128. doi:10.1038/s41467-022-30695-9.
11. Reinke et al. Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence. Eur Urol Focus. 2021 Jul;7(4):710-712. doi:10.1016/j.euf.2021.05.008.