DCA-MI (2nd Ed.) | Data Curation & Augmentation in Medical Imaging | ECCV 2026 2024 archive Subscribe
European Conference on Computer Vision | ECCV 2026 | 2nd Edition Vol. 02 / No. 01 / 2026

Data Curation
& Augmentation
in Medical Imaging.

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.

October 2026 | ECCV venue TBA Full day Successor to CVPR 2024
iMED figure showing RGB views, depth maps, aligned point clouds, geometry, and tool labels.
FIG. 01iMED RGB / Depth / Geometry
CLIMB benchmark overview with modality coverage, dataset focus areas, code, and sample data.
FIG. 02CLIMB benchmark overview
SurgVU sample frames across multiple surgical tasks.
FIG. 03SurgVU surgical tasks
IIEditionAfter CVPR 2024
3DatasetsiMED | CLIMB | SurgVU
4ThemesCuration | Restoration | 3D | Autonomy
4KeynotesConfirmed program
1DayAt ECCV 2026
§ 01About the workshop
PP. 01 - 04

From scarce data
to safer medicine.

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
§ 02Featured speakers
Keynotes & panel

Confirmed keynotes
+ panel discussion.

Sophia Bano

Sophia Bano

UCL, United Kingdom

Robot vision and scene understanding for minimally invasive surgery.
Lena Maier-Hein

Lena Maier-Hein

DKFZ / Heidelberg University, Germany

Surgical data science, benchmarking, and reproducible evaluation.
José M. M. Montiel

José M. M. Montiel

Universidad de Zaragoza, Spain

Visual SLAM, deformable SLAM for endoscopy, EndoMapper.
Mengya Xu

Mengya Xu

CUHK, Hong Kong

Medical and surgical AI across MICCAI, IPCAI, and ICRA.
§ 03Advisory board
Expanding roster
Additional advisors

More to be announced

The board will expand as additional senior advisors confirm participation.

§ 04Areas of focus
4 themes

What we want
to talk about.

01

Dataset Design & Curation

Acquisition protocols, selection strategies, annotation at scale, bias auditing, and privacy-aware sharing.

02

Visual Degradation & Restoration

Smoke, blood, specularities, blur, and super-resolution measured against downstream clinical tasks.

03

3D Understanding, Neural Rendering & SLAM

Endoscopic visual odometry, multi-camera reconstruction, deformable scene representations, and sim-to-real evaluation.

04

Autonomous Surgery & Clinical Translation

Workflow labels, imitation learning, safety-critical edges, federated evaluation, and drift monitoring.

Crosswalk
Curation
Restoration
3D & SLAM
Autonomy
Curation
Restoration
3D & SLAM
Autonomy
§ 05Featured datasets
Curation case studies

Three benchmarks,
three data lessons.

Each spotlight treats a dataset as a research contribution: acquisition decisions, ground-truth design, split strategy, and evaluation pitfalls.

iMED processing figure with RGB, depth, aligned point clouds, geometry, and tool labels.
PRIMARY SPOTLIGHT

iMED

A Multi-Endoscope Dataset for Surgical 3D Perception

340 sequences~170K frame pairs60 FPS19 methods10 ex vivo organs18 cadaveric scenes10 live porcine procedures4 camera streams

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.

Overview of the CLIMB benchmark and code.
FEATURED DATASET

CLIMB

Clinical Large-scale Integrative Multimodal Benchmark

4.51M samples19.01TB33 institutions13 domains

A multimodal clinical benchmark spanning imaging, language, time series, graph, and multimodal patient data.

arXiv:2503.07667
SurgVU sample frames from multiple surgical tasks.
FEATURED DATASET

SurgVU

Surgical Visual Understanding Dataset

840 hours~18M frames280 clips12 tools155 sessions8 tasks

Robot-assisted surgical video with tool and task labels, built for visual understanding under operating-room scale and imbalance.

arXiv:2501.09209

Figure sources: iMED local paper assets; CLIMB Figure 2 from arXiv:2503.07667; SurgVU Figure 1 from arXiv:2501.09209.

§ 06Calendar
All times AOE

Important
dates.

15 May 2026Call for papers postedOpen
01 Jul 2026Paper submission deadlineHard
01 Aug 2026Notification of acceptanceEmail
15 Aug 2026Camera-ready dueFinal
Oct 2026Workshop day at ECCV 2026In person
§ 02Important DatesAll times AOE

Calendar
at a glance.

Live countdownCalculating...
15 May 2026

Call for papers posted

OpenReview information, templates, and final submission instructions become available.

01 Jul 2026

Paper deadline

Full papers, extended abstracts, and dataset submissions due by 23:59 AOE.

01 Aug 2026

Acceptance notifications

Decisions sent by email with presentation type.

15 Aug 2026

Camera-ready due

Final PDFs uploaded and program frozen.

Oct 2026

Workshop day at ECCV

Final date confirmed once the ECCV master schedule lands.

§ 03Call for PapersOpen Spring 2026

Submit your
research.

01 - Scope

The data itself is the subject.

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.

02 - Topics

Cross-theme work is welcome.

We are especially interested in work that shows how upstream data decisions propagate into downstream clinical and surgical performance.

03 - Format

Three submission tracks.

Full papers, extended abstracts, and dataset / benchmark submissions are welcome. Final page limits and template links will be posted with the OpenReview call.

04 - Review

Double-blind review.

Each submission receives technical and domain review, with dataset papers evaluated for provenance, license clarity, and reproducibility.

05 - Ethics

Provenance is a first-class concern.

All datasets must document source, consent basis, licensing, and known coverage limits. Submissions with unclear protected-data handling may be desk-rejected.

§ 04Accepted PapersProceedings pending

Proceedings
vol. II.

P. 01 | ORAL

Accepted papers will appear here after review.

Camera-ready PDFs go live after the final program is confirmed.

P. 02 | DATASET

Dataset and benchmark submissions.

Dataset papers will include release notes, license, and baseline links.

P. 03 | POSTER

Poster track.

Poster assignments will be published with session times.

§ 05ProgramFull day

Workshop
schedule.

05ASchedule
Time | Session | Format
TimeEventType
09:00-09:1515 min

Opening remarks

Welcome and workshop overview from the organizers.

Opening
09:15-09:4530 min

Keynote 1: Dataset scarcity, design and curation in medical imaging

Invited talk on the data bottleneck for robust medical and surgical AI.

Keynote
09:45-10:3045 min

Coffee Break & Poster Session I

Interactive poster session and attendee discussion.

Poster
10:30-11:3060 min

Oral Session 1: 3D Understanding and Spatial Data

Four accepted papers, 15 minutes each.

Oral
11:30-13:0090 min

Break

Lunch break.

Break
13:00-13:3030 min

Keynote 2: SLAM, Neural Rendering

Invited talk on endoscopic geometry, mapping, and scene representation.

Keynote
13:30-14:3060 min

Data Curation for Challenge Session

iMED: Multi-Endoscope Dataset | 20 min
CLiMB: Benchmark for Colonoscopy SLAM | 20 min
SurgVU: Surgical Visual Understanding | 20 min

Dataset
14:30-15:0030 min

Coffee Break & Poster Session II

Second poster block and hallway discussion.

Poster
15:00-15:3030 min

Keynote 3: Autonomous Surgery and Clinical Translation

Invited talk on translating surgical data into robust clinical systems.

Keynote
15:30-16:3060 min

Oral Session 2: Visual degradation and restoration

Four accepted papers, 15 minutes each.

Oral
16:30-17:3060 min

Panel: Data for Surgical Autonomy, Clinical Translation

Open discussion with invited speakers and organizers.

Panel
17:30-18:0030 min

Closing Remarks & Awards

Best paper, dataset recognition, and closing remarks.

Closing
05BSpeakers
Keynotes & panel
Sophia Bano

Sophia Bano

UCL, United Kingdom

Robot vision and scene understanding for minimally invasive surgery.
Lena Maier-Hein

Lena Maier-Hein

DKFZ / Heidelberg University, Germany

Dataset scarcity, design, curation, and reproducible surgical data science.
José M. M. Montiel

José M. M. Montiel

Universidad de Zaragoza, Spain

SLAM, neural rendering, deformable reconstruction, and EndoMapper.
Mengya Xu

Mengya Xu

CUHK, Hong Kong

Autonomous surgery, clinical translation, and medical computer vision.
§ 06AwardsThree categories

Honors &
recognition.

2026 | Best Paper

TBD

Announced at the closing ceremony.

2026 | Best Dataset

TBD

Judged on novelty, provenance, license clarity, and reproducible baselines.

2026 | Audience Choice

TBD

Selected by registered attendees.

§ 07Organizers8 organizers

The team
behind it.

Fengyi Jiang

Fengyi Jiang

Primary contact | Intuitive Surgical

Sierra Bonilla

Sierra Bonilla

iMED dataset lead | UCL Hawkes Institute

Javier Morlana

Javier Morlana

CLiMB benchmark lead | Universidad de Zaragoza

Ray Zhang

Ray Zhang

Organizer | Intuitive Surgical

Mary Jin

Mary Jin

Organizer | Intuitive Surgical

Shuoqi Chen

Shuoqi Chen

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

Jingpei Lu

Jingpei Lu

Research Scientist | Intuitive Surgical

Rogerio Nespolo

Rogerio Nespolo

SurgVU lead | Intuitive Surgical

07AAdvisory Board
Invitations in progress
Sophia Bano Robot vision

Sophia Bano

UCL researcher focused on robot vision and scene understanding for minimally invasive surgery.

Profile
Lena Maier-Hein Surgical data science

Lena Maier-Hein

DKFZ and Heidelberg University researcher in surgical data science, benchmarking, and reproducible evaluation in medical AI.

Profile
José M. M. Montiel Visual SLAM

José M. M. Montiel

Universidad de Zaragoza researcher known for visual SLAM, ORB-SLAM, deformable SLAM for endoscopy, and the EndoMapper dataset.

Profile
Mengya Xu Medical AI

Mengya Xu

Chinese University of Hong Kong researcher working on medical and surgical AI, with recent work across MICCAI, IPCAI, and ICRA.

Profile
§ 08FAQsCommon questions

Frequently
asked.

Updates

Receive
updates.

Get deadline reminders, speaker announcements, and program updates by emailing the workshop contact.

Request updates

Yes. 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.

§ 09NewsUpdated weekly

What's
happening.

Website

DCA-MI 2026 design preview launched.

The second-edition site now highlights iMED, CLIMB, and SurgVU as featured dataset case studies.

Submissions

Call for papers posted.

OpenReview information, templates, and final submission instructions will be posted here.