Preface DART 2023

Lisa Koch, M. Jorge Cardoso, Enzo Ferrante, Mobarakol Islam, Meirui Jiang, Konstantinos Kamnitsas, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang

Research output: Contribution to journalEditorialpeer-review

Abstract / Description of output

Recent breakthroughs in advanced machine learning and deep learning have revolutionized computer vision and medical imaging, enabling unparalleled accuracy in tasks such as image segmentation, object recognition, disease detection, and image registration. Although these developments have greatly benefited the MICCAI community, many models suffer from limited adaptability when faced with novel scenarios or heterogeneous input data. To overcome this restriction, researchers have explored techniques such as transfer learning, representation learning, and domain adaptation, allowing for improved model training, effective domain adaptation, and the application of knowledge learned from one domain to tackle challenges in other domains. By expanding the versatility and robustness of these cutting-edge methods, researchers hope to increase their clinical utility and broaden their impact across various medical imaging applications.
Original languageEnglish
Pages (from-to)v-vi
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14293 LNCS
Publication statusPublished - 2024
Event5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023 - Vancouver, Canada
Duration: 12 Oct 202312 Oct 2023


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