  Despite the progress made in Mamba-based medical image segmentation models,
current methods utilizing unidirectional or multi-directional feature scanning
mechanisms fail to well model dependencies between neighboring positions in the
image, hindering the effective modeling of local features. However, local
features are crucial for medical image segmentation as they provide vital
information about lesions and tissue structures. To address this limitation, we
propose a simple yet effective method named SliceMamba, a locally sensitive
pure Mamba medical image segmentation model. The proposed SliceMamba includes
an efffcient Bidirectional Slice Scan module (BSS), which performs
bidirectional feature segmentation while employing varied scanning mechanisms
for distinct features. This ensures that spatially adjacent features maintain
proximity in the scanning sequence, thereby enhancing segmentation performance.
Extensive experiments on skin lesion and polyp segmentation datasets validate
the effectiveness of our method.
