A domain generalization framework that utilizes a foreground-only histogram matching protocol to resolve the domain shift issue arising from disparate clinical sources is proposed, significantly outperforming prominent domain generalization paradigms, including MixStyle and Discrete-Fourier-Transform-based frameworks.
Hong-Yi Pan, Gorkem Durak, H. Aktas et al.· 0 citations
Evaluated on four datasets, Energy-Mamba achieves state-of-the-art classification performance with significantly fewer parameters, demonstrating that physics-informed grounding can enhance both efficiency and representational quality in medical vision tasks.
State space models (SSMs) have emerged as a compelling alternative to transformers for visual recognition, offering linear computational complexity while maintaining competitive accuracy. However, the lack of interpretability tools designed specifically for the recurrent dynamics of these models remains a significant g...
M. Mabrok, Yalda Zafari· IEEE Transactions on Neural...· 2 citations
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