Call for Papers

 We would  like to invite you to submit your papers for the DART 2023 workshop to be held in October 2023 in Vancouver, Canada, as a satellite workshop of MICCAI.

Medical imaging has been revolutionized by the introduction of advanced machine learning and deep learning methodologies. Recent approaches have shown unprecedented performance gains in tasks such as segmentation, classification, detection, and registration. Although these results (obtained often on public datasets) represent important milestones for the MICCAI community, most methods lack generalization capabilities when presented with previously unseen situations (corner-cases) or different input data domains. This limits clinical applicability of these innovative approaches and therefore diminishes their impact. Transfer learning, representation learning and domain adaptation techniques have been used to tackle problems such as: model training using small datasets while obtaining generalizable representations; performing domain adaptation via few-shot learning; obtaining interpretable representations that are understood by humans; and leveraging knowledge learned from a particular domain to solve problems in another.

Through the fifth MICCAI workshop on Domain Adaptation and Representation Transfer (DART) we aim to continue the discussion forum to compare, evaluate and discuss methodological advancements and ideas that can improve the applicability of Machine Learning (ML) / Deep Learning (DL) approaches to clinical settings by making them robust and consistent across different domains.

DART welcomes all contributions in the following areas:

• Domain adaptation & domain generalisation

• Robustness

• Invariant representations

• Fair, unbiased representations

• Multi-task learning

• Disentangling representations

• Interpretable representations

• Learning from small or synthetic data

• Unsupervised Learning

• Self-Supervised Learning

• X-shot learning

• Model distillation

• Continual learning

• Meta learning

• Transfer learning

The conference program will include paper presentations, a poster session and keynote talks by prominent speakers in the field. All submitted papers will be reviewed by at least three PC members and handled by an Area Chair. Acceptance decisions will take into account paper novelty, technical depth, practical or theoretical impact. All accepted papers will be published in LNCS proceedings.

Looking forward to your submissions,

Koch, Lisa - University of Tuebingen

Kamnitsas, Konstantinos - University of Oxford 

Jiang, Meirui - The Chinese University of Hong Kong

Tsaftaris, Sotirios - University of Edinburgh

Cardoso, M. Jorge - King’s College London

Islam, Mobarakol - University College London

Rieke, Nicola - NVIDIA

Enzo, Ferrante - CONICET / Universidad Nacional del Litoral

Yang,  Dong - NVIDIA