
Sophia Bano
UCL, United Kingdom
Robot vision and scene understanding for minimally invasive surgery.A full-day ECCV workshop on data as the bottleneck for robust surgical computer vision - from curated multi-endoscope datasets to large multimodal clinical benchmarks and surgical visual understanding.
A focused program of keynotes, panels, peer-reviewed papers, and dataset spotlights.
Reliable medical systems depend on data quality as much as model novelty. Intraoperative video is scarce, expensive to annotate, and ethically restricted. DCA-MI advances data-centric methods and benchmarks as first-class research contributions, with particular attention to surgical scene understanding, endoscopic geometry, and clinical translation.
"The bottleneck is rarely the model. It is almost always the data."
Organizing Committee

UCL, United Kingdom
Robot vision and scene understanding for minimally invasive surgery.
DKFZ / Heidelberg University, Germany
Surgical data science, benchmarking, and reproducible evaluation.
Universidad de Zaragoza, Spain
Visual SLAM, deformable SLAM for endoscopy, EndoMapper.
CUHK, Hong Kong
Medical and surgical AI across MICCAI, IPCAI, and ICRA.
Featured advisor
NCT Dresden professor working on surgical data science, computer-assisted surgery, robotic vision, and AI-enabled clinical translation.
ProfileThe board will expand as additional senior advisors confirm participation.
Acquisition protocols, selection strategies, annotation at scale, bias auditing, and privacy-aware sharing.
Smoke, blood, specularities, blur, and super-resolution measured against downstream clinical tasks.
Endoscopic visual odometry, multi-camera reconstruction, deformable scene representations, and sim-to-real evaluation.
Workflow labels, imitation learning, safety-critical edges, federated evaluation, and drift monitoring.
Each spotlight treats a dataset as a research contribution: acquisition decisions, ground-truth design, split strategy, and evaluation pitfalls.

A Multi-Endoscope Dataset for Surgical 3D Perception
iMED focuses the workshop on synchronized, dual-view, deformable, specular surgical scenes. The dataset is designed to stress test visual geometry, photogrammetry, and endoscopic reconstruction beyond rigid-world assumptions.

Clinical Large-scale Integrative Multimodal Benchmark
A multimodal clinical benchmark spanning imaging, language, time series, graph, and multimodal patient data.
arXiv:2503.07667
Surgical Visual Understanding Dataset
Robot-assisted surgical video with tool and task labels, built for visual understanding under operating-room scale and imbalance.
arXiv:2501.09209Figure sources: iMED local paper assets; CLIMB Figure 2 from arXiv:2503.07667; SurgVU Figure 1 from arXiv:2501.09209.
OpenReview information, templates, and final submission instructions become available.
Full papers, extended abstracts, and dataset submissions due by 23:59 AOE.
Decisions sent by email with presentation type.
Final PDFs uploaded and program frozen.
Final date confirmed once the ECCV master schedule lands.
We invite contributions across curation, augmentation, restoration, 3D perception, learning with limited or imperfect data, and clinical translation. Submissions may be empirical, methodological, position-style, or new datasets and benchmarks with reproducible baselines.
We are especially interested in work that shows how upstream data decisions propagate into downstream clinical and surgical performance.
Full papers, extended abstracts, and dataset / benchmark submissions are welcome. Final page limits and template links will be posted with the OpenReview call.
Each submission receives technical and domain review, with dataset papers evaluated for provenance, license clarity, and reproducibility.
All datasets must document source, consent basis, licensing, and known coverage limits. Submissions with unclear protected-data handling may be desk-rejected.
Camera-ready PDFs go live after the final program is confirmed.
Dataset papers will include release notes, license, and baseline links.
Poster assignments will be published with session times.
Welcome and workshop overview from the organizers.
Invited talk on the data bottleneck for robust medical and surgical AI.
Interactive poster session and attendee discussion.
Four accepted papers, 15 minutes each.
Lunch break.
Invited talk on endoscopic geometry, mapping, and scene representation.
iMED: Multi-Endoscope Dataset | 20 min
CLiMB: Benchmark for Colonoscopy SLAM | 20 min
SurgVU: Surgical Visual Understanding | 20 min
Second poster block and hallway discussion.
Invited talk on translating surgical data into robust clinical systems.
Four accepted papers, 15 minutes each.
Open discussion with invited speakers and organizers.
Best paper, dataset recognition, and closing remarks.

UCL, United Kingdom
Robot vision and scene understanding for minimally invasive surgery.
DKFZ / Heidelberg University, Germany
Dataset scarcity, design, curation, and reproducible surgical data science.
Universidad de Zaragoza, Spain
SLAM, neural rendering, deformable reconstruction, and EndoMapper.
CUHK, Hong Kong
Autonomous surgery, clinical translation, and medical computer vision.Announced at the closing ceremony.
Judged on novelty, provenance, license clarity, and reproducible baselines.
Selected by registered attendees.

Primary contact | Intuitive Surgical

iMED dataset lead | UCL Hawkes Institute

CLiMB benchmark lead | Universidad de Zaragoza

Organizer | Intuitive Surgical

Organizer | Intuitive Surgical

Computer Vision & Medical Imaging engineer at Intuitive Surgical, specializing in advanced imaging and robotic-assisted procedures. CMU Robotics Institute alumnus; presenter and reviewer across IEEE TRO, IROS, CVPR, and ICML.
shuoqi.chen@intusurg.com

Research Scientist | Intuitive Surgical

SurgVU lead | Intuitive Surgical
Featured advisor
Professor for Translational Surgical Oncology at NCT Dresden, working on surgical data science, computer-assisted surgery, robotic vision, and AI-enabled clinical translation.
Profile
Robot vision
UCL researcher focused on robot vision and scene understanding for minimally invasive surgery.
Profile
Surgical data science
DKFZ and Heidelberg University researcher in surgical data science, benchmarking, and reproducible evaluation in medical AI.
Profile
Visual SLAM
Universidad de Zaragoza researcher known for visual SLAM, ORB-SLAM, deformable SLAM for endoscopy, and the EndoMapper dataset.
Profile
Medical AI
Chinese University of Hong Kong researcher working on medical and surgical AI, with recent work across MICCAI, IPCAI, and ICRA.
ProfileFor submission questions, sponsorship inquiries, or program updates, contact the organizing team directly.
workshop@dca-in-mi.orgGet deadline reminders, speaker announcements, and program updates by emailing the workshop contact.
Request updatesYes. The workshop is non-archival; concurrent submission is allowed with disclosure.
Yes. Dataset and benchmark submissions are a central part of the workshop.
Authors are expected to attend in person. Keynote recording availability depends on speaker consent.
Still stuck? Write to workshop@dca-in-mi.org.
The second-edition site now highlights iMED, CLIMB, and SurgVU as featured dataset case studies.
OpenReview information, templates, and final submission instructions will be posted here.