  The accurate segmentation of medical images is crucial for diagnosing and
treating diseases. Recent studies demonstrate that vision transformer-based
methods have significantly improved performance in medical image segmentation,
primarily due to their superior ability to establish global relationships among
features and adaptability to various inputs. However, these methods struggle
with the low signal-to-noise ratio inherent to medical images. Additionally,
the effective utilization of channel and spatial information, which are
essential for medical image segmentation, is limited by the representation
capacity of self-attention. To address these challenges, we propose a
multi-dimension transformer with attention-based filtering (MDT-AF), which
redesigns the patch embedding and self-attention mechanism for medical image
segmentation. MDT-AF incorporates an attention-based feature filtering
mechanism into the patch embedding blocks and employs a coarse-to-fine process
to mitigate the impact of low signal-to-noise ratio. To better capture complex
structures in medical images, MDT-AF extends the self-attention mechanism to
incorporate spatial and channel dimensions, enriching feature representation.
Moreover, we introduce an interaction mechanism to improve the feature
aggregation between spatial and channel dimensions. Experimental results on
three public medical image segmentation benchmarks show that MDT-AF achieves
state-of-the-art (SOTA) performance.
