Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

The 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, brought together experts to discuss methodological advancements and ideas to improve the applicability of ML/DL approaches in clinical settings. The workshop accepted 13 papers out of 25 submissions, highlighting the importance of robust and consistent ML/DL solutions across different domains.

Format: Paperback / softback
Length: 147 pages
Publication date: 20 September 2022
Publisher: Springer International Publishing AG

The 4th MICCAI Workshop on Domain Adaptation and Representation Transfer (DART 2022) was held in conjunction with MICCAI 2022 in September 2022, with the aim of fostering a discussion forum to compare, evaluate, and explore innovative approaches and ideas that enhance the robustness and consistency of machine learning (ML)/deep learning (DL) methods in clinical settings across diverse domains.

DART 2022 received a total of 25 submissions, out of which 13 papers were accepted for presentation. The workshop's objective was to bring together experts from various fields to discuss and explore methodological advancements and concepts that can improve the application of ML/DL techniques in clinical settings, ensuring their reliability and adaptability across different domains.

The workshop featured a diverse range of presentations and discussions, covering topics such as transfer learning, domain adaptation, adversarial learning, and unsupervised learning. The presenters showcased their latest research findings and discussed the challenges and opportunities associated with applying ML/DL in healthcare and other medical applications.

One of the key themes of the workshop was the importance of data representation and feature extraction in domain adaptation. The presenters emphasized the need for robust and generalizable feature representations that can capture the inherent characteristics of different domains and facilitate accurate model predictions.

Another important aspect of the workshop was the evaluation of domain adaptation methods. The presenters discussed various evaluation metrics and frameworks that can be used to assess the performance of ML/DL models in different domains and scenarios. They also discussed the challenges associated with evaluating domain adaptation, such as the lack of labeled data and the potential for domain shift.

The workshop also highlighted the importance of interdisciplinary collaboration in developing effective domain adaptation solutions. The presenters emphasized the need for collaboration between computer scientists, medical professionals, and domain experts to ensure that ML/DL methods are developed and applied in a way that is meaningful and impactful to clinical practice.

Overall, the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer was a successful event that brought together experts from around the world to explore innovative approaches and ideas in the field of ML/DL in clinical settings. The workshop's findings and discussions will contribute to the ongoing development of ML/DL methods and their application in healthcare and other medical domains.
The 4th MICCAI Workshop on Domain Adaptation and Representation Transfer (DART 2022) was held in conjunction with MICCAI 2022 in September 2022, with the aim of fostering a discussion forum to compare, evaluate, and explore innovative approaches and ideas that enhance the robustness and consistency of machine learning (ML)/deep learning (DL) methods in clinical settings across diverse domains.

DART 2022 received a total of 25 submissions, out of which 13 papers were accepted for presentation. The workshop's objective was to bring together experts from various fields to discuss and explore methodological advancements and concepts that can improve the application of ML/DL techniques in clinical settings, ensuring their reliability and adaptability across different domains.

The workshop featured a diverse range of presentations and discussions, covering topics such as transfer learning, domain adaptation, adversarial learning, and unsupervised learning. The presenters showcased their latest research findings and discussed the challenges and opportunities associated with applying ML/DL in healthcare and other medical applications.

One of the key themes of the workshop was the importance of data representation and feature extraction in domain adaptation. The presenters emphasized the need for robust and generalizable feature representations that can capture the inherent characteristics of different domains and facilitate accurate model predictions.

Another important aspect of the workshop was the evaluation of domain adaptation methods. The presenters discussed various evaluation metrics and frameworks that can be used to assess the performance of ML/DL models in different domains and scenarios. They also discussed the challenges associated with evaluating domain adaptation, such as the lack of labeled data and the potential for domain shift.

The workshop also highlighted the importance of interdisciplinary collaboration in developing effective domain adaptation solutions. The presenters emphasized the need for collaboration between computer scientists, medical professionals, and domain experts to ensure that ML/DL methods are developed and applied in a way that is meaningful and impactful to clinical practice.

Overall, the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer was a successful event that brought together experts from around the world to explore innovative approaches and ideas in the field of ML/DL in clinical settings. The workshop's findings and discussions will contribute to the ongoing development of ML/DL methods and their application in healthcare and other medical domains.

Weight: 256g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031168512
Edition number: 1st ed. 2022