  Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of
applications relied on high-precision pathology image segmentation, such as
computational pathology and precision medicine. Since hyperspectral pathology
images benefit from the rich and detailed spectral information even beyond the
visible spectrum, the key to achieve high-precision hyperspectral pathology
image segmentation is to felicitously model the context along high-dimensional
spectral bands. Inspired by the strong context modeling ability of
transformers, we hereby, for the first time, formulate the contextual feature
learning across spectral bands for hyperspectral pathology image segmentation
as a sequence-to-sequence prediction procedure by transformers. To assist
spectral context learning procedure, we introduce two important strategies: (1)
a sparsity scheme enforces the learned contextual relationship to be sparse, so
as to eliminates the distraction from the redundant bands; (2) a spectral
normalization, a separate group normalization for each spectral band, mitigates
the nuisance caused by heterogeneous underlying distributions of bands. We name
our method Spectral Transformer (SpecTr), which enjoys two benefits: (1) it has
a strong ability to model long-range dependency among spectral bands, and (2)
it jointly explores the spatial-spectral features of HSI. Experiments show that
SpecTr outperforms other competing methods in a hyperspectral pathology image
segmentation benchmark without the need of pre-training. Code is available at
https://github.com/hfut-xc-yun/SpecTr.
