Call for papers

The ECCV-MCV workshop will provide an opportunity to students, researchers, and developers in academia, industry, and healthcare to discuss and learn about recent advancements in computer vision for medical image analysis and computer-assisted interventions. The ultimate goal of the workshop is to bring together stakeholders interested in leveraging big data, machine learning, and computer vision techniques to build the next generation of tools and products to advance image-based healthcare. We strongly encourage authors to improve the reproducibility of their research by considering open data, open implementations, and appropriate evaluation design and reporting. Where possible, we invite authors to use open data or to make their data and code available for open access by other researchers.

The proceedings will be published by Springer under the “Lecture Notes in Computer Science” book series.

The MCV-ECCV workshop will be held in conjunction with ECCV 2022, which is to take place in Tel-Aviv, Israel in Oct, 2022.

We invite submissions of long papers adhering to the ECCV 2022 paper submission style, format, and length restrictions on any aspect of biomedical computer vision. Submissions will be handled through CMT3. The papers will be evaluated by external reviewers and meta reviewers for inclusion in the ECCV Workshop Proceedings.

Link to LateX template and sample submission here.

Important Dates

Tentative paper submission schedule.

Submission: July 11, 2022, Anywhere on Earth

Notification of acceptance: August 15, 2022

Camera ready: August 22, 2022

About the workshop


The MCV workshop will provide an opportunity to students, researchers and developers in biomedical imaging companies to present, discuss and learn recent advancements in medical image analysis. The ultimate goal of the workshop is leveraging big data, deep learning and novel representation to effectively build the next generation of robust quantitative medical imaging parsing tools and products. Prominent applications include large scale cancer screening, computational heart modeling, landmark detection, neural structure and functional labeling and image-guided intervention. Computer Vision advancements and Deep learning in particular are rapidly transitioned to the medical imaging community in recent years. Additionally, there is a tremendous growth in startup activity applying medical computer vision algorithms to the healthcare industry. Collecting and accessing radiological patient images is a challenging task. Recent efforts include VISCERAL Challenge and Alzheimer’s Disease Neuroimaging Initiative. The NIH and partners are working on extracting trainable anatomical and pathological semantic labels from radiology reports that are linked to patients’ CT/MRI/X-ray images or volumes such as NCI’s Cancer Imaging Archive. The MCV workshop aims to encourage the establishment of public medical datasets to be used as unbiased platforms to compare performances on the same set of data for various disease findings.


  • Advances in Machine Learning Theory and Methods for medical imaging
  • Deep Learning (e.g., architectures, generative models, optimization for deep networks)
  • Reinforcement Learning (e.g., decision and control, planning, hierarchical RL, robotics)
  • Few shot learning
  • Federated learning
  • NLP
  • Self, Semi and Weakly supervised approaches
  • Image Synthesis
  • Classification and Detection
  • Image Reconstruction
  • Image Registration
  • Image Segmentation
  • Biological and Cell Microscopy Imaging Analysis
  • Mixed, Augmented and Virtual Reality
  • Shape Analysis
  • Computational (Integrative) Pathology
  • Computational Anatomy and Physiology
  • Imaging Biomarkers
  • Computational Neuroscience and cognitive science
  • Computer Aided Diagnosis
  • Interventional Simulation Systems
  • Image-Guided Interventions and Robot-Assisted Surgery
  • Population Imaging and Imaging Genetics
  • Surgical Data Science
  • Biological and Cell Microscopy Imaging Analysis
  • Mixed, Augmented and Virtual Reality
  • Outcome/disease prediction
Social Aspects of Machine Learning
  • Safety, Fairness, Privacy, Ethics
  • Explainability
  • Human-AI interaction
  • Visualization in Biomedical Imaging



Prof. Daniel Alexander



Prof. Yonina Eldar

Weizmann Institute



Tal Arbel

McGill University


Ayelet Akselrod-Balin

Reichman University


Vasileios Belagiannis

Otto von Guericke University


Qi Dou

Chinese University of Hong Kong


Moti Freiman

Technion - Israel Institute of Technology


Nicolas Padoy

University of Strasbourg & IHU Strasbourg


Tammy Riklin-Raviv

Ben-Gurion University


Mathias Unberath

Johns Hopkins University


Yuyin Zhou

University of California, Santa Cruz

Program Committee