Medical hyperspectral image classification based weakly supervised single-image global learning network

Medical hyperspectral imaging provides new possibilities for non-invasive detection and characterization of diseases, and the processing of images can be accelerated and rationalized by using deep learning technology to classify pixels as one tissue or another, or as lesion or healthy tissue. However, most current methods for intelligently identifying pixels are not robust to large variations in pixel intensity within an image, particularly local learning approaches that rely on pixel or patch input. In this paper, we propose a network being able to learn to classify all pixels on an image by training with only a small number of manually labeled pixels in the same image. The network contains a hard band attention module (HBAM) to eliminate noisy bands and a dual-kernel spatial–spectral fusion attention module (DK-SSFAM) which uses two convolution kernels to weight spatial and spectral features and integrates them accordingly. We demonstrate that our proposed weakly supervised single-image global learning (SiGL) network classifies pixels in hyperspectral images of human brain in vivo better than traditional deep learning methods, suggesting potential for the clinic.