  Deep neural networks (DNNs) have been widely adopted in brain lesion
detection and segmentation. However, locating small lesions in 2D MRI slices is
challenging, and requires to balance between the granularity of 3D context
aggregation and the computational complexity. In this paper, we propose a novel
view-disentangled transformer to enhance the extraction of MRI features for
more accurate tumour detection. First, the proposed transformer harvests
long-range correlation among different positions in a 3D brain scan. Second,
the transformer models a stack of slice features as multiple 2D views and
enhance these features view-by-view, which approximately achieves the 3D
correlation computing in an efficient way. Third, we deploy the proposed
transformer module in a transformer backbone, which can effectively detect the
2D regions surrounding brain lesions. The experimental results show that our
proposed view-disentangled transformer performs well for brain lesion detection
on a challenging brain MRI dataset.
