DCA-MI (2nd Ed.) | Data Curation & Augmentation in Medical Imaging | ECCV 2026 2024 archive

Data Curation
& Augmentation
in Medical Imaging.

An ECCV workshop on the data bottleneck for robust medical imaging AI.

September 8-9, 2026 | ECCV Venue Malmö, Sweden Successor to CVPR 2024
iMED figure showing RGB views, depth maps, aligned point clouds, geometry, and tool labels.
FIG. 01iMED RGB / Depth / Geometry
CLiMB2026 colonoscopy localization and mapping benchmark overview.
FIG. 02CLiMB2026 overview
SurgVU sample frames across multiple surgical tasks.
FIG. 03SurgVU surgical tasks

Invited speakers

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 AI across MICCAI, IPCAI, and ICRA.

Organizers

Fengyi Jiang

Fengyi Jiang

Primary contact | Intuitive Surgical

Sierra Bonilla

Sierra Bonilla

iMED dataset lead | UCL Hawkes Institute
Profile

Javier Morlana

Javier Morlana

CLiMB2026 lead | Universidad de Zaragoza
Profile

Xiaorui Zhang

Xiaorui Zhang

SurgVU Challenge organizer | Intuitive Surgical

Mary Jin

Mary Jin

Vision system analyst | Intuitive Surgical

Shuoqi Chen

Shuoqi Chen

Computer Vision & Medical Imaging engineer | Intuitive Surgical

Jingpei Lu

Jingpei Lu

Research Scientist | Intuitive Surgical

Rogerio Nespolo

Rogerio Nespolo

Machine learning engineer | Intuitive Surgical

Areas of focus

Dataset curation, benchmarking, and responsible data practices

Synthetic and simulated data

3D perception and spatial medical imaging

Learning, multimodal data, and clinical translation

Advisory board

Sophia BanoInvited speaker

Sophia Bano

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

Lena Maier-HeinInvited speaker

Lena Maier-Hein

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

José M. M. MontielInvited speaker

José M. M. Montiel

Universidad de Zaragoza researcher known for visual SLAM, deformable SLAM for endoscopy, and EndoMapper.

Mengya XuInvited speaker

Mengya Xu

Chinese University of Hong Kong researcher working on medical AI across clinical and surgical applications.

Danail StoyanovAdvisor

Danail Stoyanov

UCL Professor of Robot Vision, Co-Director of the UCL Hawkes Institute, and Royal Academy of Engineering Chair in Emerging Technologies.

Profile

Highlighted datasets and challenges

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

iMED

Multi-Endoscope Dataset for 3D Perception

340 sequences~170K timepoints4 views per timepoint2 challenge tasksEx vivoPostmortemLive settingsEndoscopic 3D

iMED2026 is a MICCAI/EndoVis challenge on synchronized multi-endoscope data, with relative pose estimation and deformable novel view synthesis tracks for endoscopic 3D perception.

Challenge site
CLiMB2026 colonoscopy localization and mapping benchmark overview.
FEATURED BENCHMARK

CLiMB2026

Colonoscopy Localization and Mapping Benchmark

ColonoscopyLocalizationMappingUnder development

CLiMB2026 is an unpublished benchmark under development for colonoscopy localization and mapping. Public dataset access, paper details, and benchmark statistics will be added after organizer review.

SurgVU sample frames from multiple surgical tasks.
FEATURED DATASET

SurgVU

Surgical Visual Understanding Dataset Series

840 hours~18M frames280 clips12 tools155 sessions8 tasks

SurgVU focuses on large-scale surgical video understanding. DCA-MI highlights it as a dataset case study for real-world video curation and evaluation.

arXiv:2501.09209

Important dates

15 May 2026 — Call for papers posted

01 Jul 2026 — Paper submission deadline

01 Aug 2026 — Notification of acceptance

15 Aug 2026 — Camera-ready due

08–09 Sep 2026 — Workshop at ECCV 2026

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

iMED

Multi-Endoscope Dataset for 3D Perception

340 sequences~170K timepoints4 views per timepoint2 challenge tasksEx vivoPostmortemLive settingsEndoscopic 3D

iMED2026 is a MICCAI/EndoVis challenge on synchronized multi-endoscope data, with relative pose estimation and deformable novel view synthesis tracks for endoscopic 3D perception.

Challenge site
CLiMB2026 colonoscopy localization and mapping benchmark overview.
FEATURED BENCHMARK

CLiMB2026

Colonoscopy Localization and Mapping Benchmark

ColonoscopyLocalizationMappingUnder development

CLiMB2026 is an unpublished benchmark under development for colonoscopy localization and mapping. Public dataset access, paper details, and benchmark statistics will be added after organizer review.

SurgVU sample frames from multiple surgical tasks.
FEATURED DATASET

SurgVU

Surgical Visual Understanding Dataset Series

840 hours~18M frames280 clips12 tools155 sessions8 tasks

SurgVU focuses on large-scale surgical video understanding. DCA-MI highlights it as a dataset case study for real-world video curation and evaluation.

arXiv:2501.09209

15 May 2026 — Call for papers posted

01 Jul 2026 — Paper submission deadline

01 Aug 2026 — Notification of acceptance

15 Aug 2026 — Camera-ready due

08–09 Sep 2026 — Workshop at ECCV 2026

Submission tracks

Original Work Track

New peer-reviewed submissions

New work prepared for DCA-MI in ECCV workshop format, reviewed through a double-blind OpenReview workflow by the program committee. Full papers, extended abstracts, and dataset or benchmark papers are welcome.

Published Work Track

Recent work posters

Relevant papers already peer-reviewed at major computer vision, machine learning, medical imaging, robotics, or clinical AI venues and journals. These submissions are screened for topical fit and poster availability rather than re-reviewed.

Topics of interest

  • Acquisition pipelines, annotation strategy, and multi-site dataset construction
  • Pseudo-ground-truth generation, calibration, and quality control
  • Clinically meaningful metrics, splits, leakage analysis, and reproducibility reporting
  • Human-in-the-loop and clinician-grounded evaluation methodologies
  • Privacy, fairness, federated learning, and responsible dataset governance
  • Generative and diffusion-based augmentation for images and video
  • Synthetic data for rare diseases and under-represented populations
  • Physics-based simulation, digital twins, and clinical simulators
  • Sim-to-real adaptation and patient-specific synthetic priors
  • SLAM and structure-from-motion for endoscopy and interventional imaging
  • Neural rendering, Gaussian splatting, and implicit 3D representations
  • Calibration and pose estimation under deformation, smoke, and reflection
  • Dataset and pseudo-GT design for reconstruction benchmarks
  • Self-, semi-, weakly-supervised learning and foundation-model adaptation
  • Domain adaptation, distribution shift, and out-of-distribution detection
  • Multimodal clinical datasets, vision-language models, and workflow understanding
  • Deployment safety, uncertainty quantification, and bench-to-bedside evaluation
Fengyi Jiang

Fengyi Jiang

Primary contact | Intuitive Surgical

Sierra Bonilla

Sierra Bonilla

iMED dataset lead | UCL Hawkes Institute
Profile

Javier Morlana

Javier Morlana

CLiMB2026 lead | Universidad de Zaragoza
Profile

Xiaorui Zhang

Xiaorui Zhang

Machine Learning Engineer at Intuitive Surgical and primary organizer of the SurgVU Challenge at MICCAI 2026. He is also the primary author of SurgiSR4K, the first 4K surgical imaging and video dataset. He holds graduate degrees in Robotics and Computer Science from Johns Hopkins University and has extensive experience in computer vision, video analysis, and vision-language models for medical applications.

Mary Jin

Mary Jin

Vision system analyst at Intuitive Surgical. Her research interests are in computational imaging, specifically the joint design of optics and image processing. She has published in venues such as PNAS, Nature Communications, and CVPR.

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

Machine Learning Engineer at Intuitive Surgical, working at the crossroads of surgical data science and AI. His research focuses on real-time surgical guidance and surgeon skill assessment for eye surgery and robot-assisted procedures using multiple data modalities, including image, kinematics, and visual attention, through computer vision and deep learning. His interests also include the investigation of biased datasets in the surgical field.

07AAdvisory Board
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 AI across clinical and surgical applications, with recent work across MICCAI, IPCAI, and ICRA.

Profile
Danail Stoyanov Robot vision

Danail Stoyanov

UCL Professor of Robot Vision, Co-Director of the UCL Hawkes Institute, and Royal Academy of Engineering Chair in Emerging Technologies, focused on surgical robotics and AI for minimally invasive interventions.

Profile