1. The paper proposes DAE-Former, a novel method that seeks to efficiently design the self-attention mechanism in Transformers.
2. DAE-Former captures both spatial and channel relations across the whole feature dimension while staying computationally efficient.
3. DAE-Former outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets without requiring pre-training weights.
The article is generally trustworthy and reliable, as it provides evidence for its claims in the form of experiments conducted on two datasets (multi-organ cardiac and skin lesion segmentation). The authors also provide a link to their code, which further adds to the trustworthiness of the article. Furthermore, there are no unsupported claims or missing points of consideration in the article.
However, there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or present any alternative approaches to their proposed method. Additionally, they do not discuss any possible risks associated with their approach or note any limitations of their work. Finally, there is no mention of how their proposed method compares to existing methods in terms of computational complexity or time efficiency.