Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images
Designing a single domain generalization (DG) framework that generalizes from one source domain to arbitrary unseen domains is practical yet challenging in medical image segmentation, mainly due to the domain shift and limited source domain information. To tackle these issues, we reason that domain-...
Main Authors: | Guo, X, Liu, J, Yuan, Y |
---|---|
Format: | Journal article |
Language: | English |
Published: |
Springer
2024
|
Similar Items
-
Medical domain knowledge in domain-agnostic generative AI
by: Jakob Nikolas Kather, et al.
Published: (2022-07-01) -
Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
by: Xiaoyu Hou, et al.
Published: (2021-12-01) -
Domain-Agnostic Representation of Side-Channels
by: Aaron Spence, et al.
Published: (2024-08-01) -
An adaptive ensemble feature selection technique for model-agnostic diabetes prediction
by: K. Natarajan, et al.
Published: (2025-02-01) -
Domain-agnostic procedural content generation can be done declaratively
by: Angilica, D, et al.
Published: (2023)