1st MICCAI Workshop on
“Distributed And Collaborative Learning”

Important Dates

- Submission Deadlines:

- Intention of submission 9th of July 2020
- Full paper submission 15th of July 2020

- Notification of Acceptance: 6th of August 2020
- Camera Ready: 15th of August 2020
- Workshop Date: 4 October 2020 (pm)

News

[July] Due to several requests, we decided to extend the submission deadline. Please note that the intention of submission still has to be entered into the CMT system until 9th of July 2020 and the full paper until 15th of July 2020.
[June] Submission deadline has been updated. Please submit your paper here. All MICCAI workshops - including DCL - will take place virtually this year.
[May] MICCAI's workshop schedule has been announced. The DCL workshop will take place on October 4 in the afternoon.
[April] Our first keynote speaker is confirmed! Dzung Pham will talk about governance and ethics in the era of distributed learning.
[March] The DCL Workshop has been approved for MICCAI 2020  

Distributed and Collaborative Learning

Deep learning empowers enormous scientific advances, with key approcations in healthcare. It has been widely accepted that it is possible to achieve better models with growing amounts of data. However, enabling learning on these huge datasets or training huge models in a timely manner requires to distribute the learning on several devices. One particularity in the medical domain, and in the medical imaging setting is that data sharing across different institutions often becomes impractical due to strict privacy regulations, making the collection of large-scale centralized datasets practically impossible.

Some of the problems, therefore, become: how can we train models in a distributed way on several devices? And is it possible to achieve models as strong as those that can be trained on large centralized datasets without sharing data and breaching the restrictions on privacy and property? Distributed machine learning, including Federated Learning (FL) approaches, could be helpful to solve the latter problem. Different institutions can contribute to building more powerful models by performing collaborative training without sharing any training data. The trained model can be distributed across various institutions but not the actual data. We hope that with FL and other forms of distributed and collaborative learning, the objective of training better, more robust models of higher clinical utility while still protecting the privacy within the data can be achieved.

Call for Papers

Through the first MICCAI Workshop on Distributed And Collaborative Learning” (DCL) we aim to provide a discussion forum to compare, evaluate and discuss methodological advancements and ideas around federated, distributed, and collaborative learning schemes that are applicable in the medical domain.

  • Federated, distributed learning, and other forms of collaborative learning
  • Server-client and peer-to-peer learning
  • Advanced data and model parallelism learning techniques
  • Optimization methods for distributed or collaborative learning
  • Privacy-preserving technique and security for distributed or collaborative learning
  • Efficient communication and learning (multi-device, multi-node)
  • Adversarial attacks on distributed or collaborative learning
  • Dealing with unbalanced (non-IID) data in collaborative learning
  • Asynchronous learning
  • Software tools and implementations of distributed or collaborative learning
  • Model sharing techniques, sparse/partial learning of models
  • Applications of distributed/collaborative learning techniques: multi-task learning, model agnostic learning, meta-learning, etc.

Preliminary Program

XX:XX - Welcome and opening
XX:XX - Keynote session
XX:XX - Oral session 1
XX:XX - Break & Poster Session
XX:XX - Oral session 2
XX:XX - Best paper award
XX:XX - Concluding Remarks

Keynote Session

Dzung Pham

National Institutes of Health

Governance & Ethics in the Rise of Distributed Learning

Meet the Organising Team

Shadi Albarqouni

TUM/ETH Zürich

M. Jorge Cardoso

King’s College London

Wenqi Li

NVIDIA

Nicola Rieke

NVIDIA

Daguang Xu

NVIDIA

Spyridon Bakas

University of Pennsilvania 

Bennett Landman

Vanderbilt University

Fausto Milletari

Verb Surgical

Holger Roth

NVIDIA

Contact: nrieke(at)nvidia.com

Submission Guidelines

Format: Papers will be submitted electronically following Lecture Notes in Computer Science (LNCS) style of up to 8 + 2 pages (same as MICCAI 2020 format). Submissions exceeding page limit will be rejected without review. Latex style files can be found from Springer, which also contains Word instructions. The file format for submissions is Adobe Portable Document Format (PDF). Other formats will not be accepted.

Double Blind Review: DCL reviewing is double blind. Please review the Anonymity guidelines of MICCAI main conference, and confirm that the author field does not break anonymity.

Paper Submission: DCL uses the CMT system for online submission.

Supplemental Material: Supplemental material submission is optional, following same deadline as the main paper. Contents of the supplemental material would be referred to appropriately in the paper, while reviewers are not obliged to read them.

Submission Originality: Submissions should be original, no paper of substantially similar content should be under peer review or has been accepted for a publication elsewhere (conference/journal, not including archived work).

Proceedings: The proceedings of DCL 2020 will be published as part of the joint MICCAI Workshops proceedings with Springer (LNCS)

Publication Strategy

Springer LNCS

Papers will be published as part of the MICCAI Satellite Events joint LNCS proceedings.

Review Process

All papers will be reviewed following a double-blind review process with at least 2 reviewers per submission.
We follow the MICCAI 2020 guideline regarding arXiv: Reviewers are strongly discouraged to search arXiv for submissions they are responsible to review. Even if they come across this information accidentally, they are discouraged to use the information in formulating their informed review of submissions. arXiv papers are not considered prior work since they have not been peer-reviewed. Therefore, citations to those papers are not required and reviewers are asked to not penalize a paper that fails to cite an arXiv submission.
Each reviewer will be able to cast a score from 1 (lowest) to 5 (highest) and papers with average scores higher than 2.5 will be considered acceptable.
The final decision about acceptance/rejection will be made by the PC member according to ranking, quality and the total number of submissions.
Outstanding papers will be selected for an oral presentation.
We will select reviewers from a pool of reputable researchers in the field who have repeatedly published at venues such as MICCAI, MIDL, CVPR, ICCV, IPMI, and ECCV. The review process will be implemented through the CMT platform. We will use the same system to match papers to the appropriate reviewers.