Thanks to all the speakers who helped make this virtual event a real success! All the talks can still be watched through the links below.

The workshop was featured in the BEST OF ECCV of Computer Vision News


Workshop is generously supported by:

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We congratulate the winners of the best paper awards

Best paper

Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound

Zeyu Fu; Jianbo Jiao; Robail Yasrab; Lior Drukker; Aris Papageorghiou; Alison Noble


Simultaneous Detection and Classification of Partially and Weakly Supervised Cells

Alona Golts; Ido Livneh; Aaron Ciechanover; Michael Elad

qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment

Yael Zaffrani-Reznikov; Onur Afacan; Sila Kurugol; Simon K Warfield; Moti Freiman

Workshop Schedule

Video links will be live on October 23rd
9:00-9:10 Presenters set up
9:10-9:15 Opening Remarks - Prof. Tal Arbel
9:15-9:45 Invited talk 1: Prof. Yonina Eldar, Weizmann Institute, Israel.
“Model Based Deep Learning for Medical Diagnosis”
9:45-10:45 Session 1:
Chairs: Prof. Tammy Riklin-Raviv, Dr. Moti Freiman
9:45-10:00 “Beyond Local Processing: Adapting CNNs for CT Reconstruction” (video link)
Bassel Hamoud, Yuval Bahat, Tomer Michaeli. Technion - Israel Institute of Technology.
10:00-10:15 “Simultaneous Detection and Classification of Partially and Weakly Supervised Cells” (video link)
Alona Golts, Ido Livneh, Yaniv Zohar, Aaron Ciechanover, Michael Elad. Technion - Israel Institute of Technology.
10:15-10:30 “Joint Calibrationless Reconstruction and Segmentation of Parallel MRI” (video link)
Aniket Pramanik, Mathews Jacob. The University of Iowa, IA, USA
10:30-10:45 “Multi-Scale Multi-Task distillation for incremental 3D medical image segmentation” (video link)
Mu Tian, Qinzhu Yang, Yi Gao. Shenzen Univ. China.
10:45-11:45 Session 2:
Live Q&A + Coffee break + poster session (video links)
11:45-12:15 Invited talk 2: Prof. Alex Frangi, University of Leeds, UK.
“Precision Imaging for next Generation Regulation of Medical Products”
12:15-12:45 Industrial session:
Chairs: Dr. Ayelet Akselrod-Ballin, Dr. Moti Freiman
12:15-12:30 GE Healthcare
12:30-12:45 Asensus Surgical Ltd
12:45-14:15 Lunch Break
14:15-15:30 Session 3:
Chairs: Prof. Tal Arbel, Prof. Tammy Riklin-Raviv
14:15-14:30 “Estimating Withdrawal Time in Colonoscopies” (video link)
Liran Katzir, Danny Veikherman, Valentin Dashinsky, Roman Goldenberg, Ilan Shimshoni, Nadav Rabani, Regev Cohen, Ori Kelner, Ehud Rivlin and Daniel Freedman. Google.
14:30-14:45 “qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment” (video link)
Yael Zaffrani-Reznikov, Onur Afacan, Sila Kurugol, Simon Warfield, Moti Freiman. Technion - Israel Institute of Technology.
14:45-15:00 “Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound” (video link)
Zeyu Fu, Jianbo Jiao, Robail Yasrab, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble. University of Oxford, UK.
15:00-16:00 Session 4:
Live Q&A + coffee break, posters session (video links)
16:00 - 16:45 Panel: Career perspective.
Prof. Leo Joskowicz, The Hebrew Univ. of Jerusalem
Prof. Hayit Greenspan, Tel-Aviv University
Prof. Ilan Shelef, Soroka Medical Center and Ben-Gurion University
Prof. Ron Kikinis, Harvard Medical School,
Prof. William (Sandy) Wells, Harvard Medical School,
Prof. Tal Arbel, Mcgill University

Moderators: Dr. Ayelet Akselrod-Ballin, Prof. Tammy Riklin-Raviv, Dr. Moti Freiman
16:45 Best paper award, Closing Remarks
17:00 Happy hour

List of Accepted Papers

Full list here

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.


Papers addressing topics related to medical computer vision are invited. The topics include, but are not limited to:

General (with relation to medical imaging)
  • Deep Learning (e.g., architectures, transformers, optimization for deep networks)
  • Reinforcement Learning (e.g., decision and control, planning, hierarchical RL, robotics)
  • Few shot learning
  • Federated learning
  • Merging clinical data, textual data and imaging
  • Domain adaption
  • Representation learning
  • Uncertainty estimation
  • Active learning
  • Safety, fairness, privacy, ethics
  • Explainability,
  • Human-AI interaction
  • 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
  • Image Synthesis
  • Classification and Detection
  • Image Reconstruction
  • Image Registration
  • Semantic/Instance Segmentation and Detection
  • Biological and Cell Microscopy Imaging Analysis
  • Mixed, Augmented and Virtual Reality
  • Shape Analysis



Prof. Yonina Eldar

Weizmann Institute


Prof. Alejandro Frangi

University of Leeds



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