RESUMO
Recent works on semantic segmentation witness significant performance improvement by utilizing global contextual information. In this paper, an efficient multi-granularity based semantic segmentation network (MGSeg) is proposed for real-time semantic segmentation, by modeling the latent relevance between multi-scale geometric details and high-level semantics for fine granularity segmentation. In particular, a light-weight backbone ResNet-18 is first adopted to produce the hierarchical features. Hybrid Attention Feature Aggregation (HAFA) is designed to filter the noisy spatial details of features, acquire the scale-invariance representation, and alleviate the gradient vanishing problem of the early-stage feature learning. After aggregating the learned features, Fine Granularity Refinement (FGR) module is employed to explicitly model the relationship between the multi-level features and categories, generating proper weights for fusion. More importantly, to meet the real-time processing, a series of light-weight strategies and simplified structures are applied to accelerate the efficiency, including light-weight backbone, channel compression, narrow neck structure, and so on. Extensive experiments conducted on benchmark datasets Cityscapes and CamVid demonstrate that the proposed method achieves the state-of-the-art performance, 77.8%@50fps and 72.7%@127fps on Cityscapes and CamVid datasets, respectively, having the capability for real-time applications.
RESUMO
Inflammatory bowel disease (IBD), which mainly includes ulcerative colitis (UC) and Crohn's disease (CD), is a group of chronic bowel diseases that are characterized by abdominal pain, diarrhea, and bloody stools. IBD is strongly associated with depression, and its patients have a higher incidence of depression than the general population. Depression also adversely affects the quality of life and disease prognosis of patients with IBD. The tryptophan-kynurenine metabolic pathway degrades more than 90% of tryptophan (TRP) throughout the body, with indoleamine 2,3-dioxygenase (IDO), the key metabolic enzyme, being activated in the inflammatory environment. A series of metabolites of the pathway are neurologically active, among which kynerunic acid (KYNA) and quinolinic acid (QUIN) are molecules of great interest in recent studies on the mechanisms of inflammation-induced depression. In this review, the relationship between depression in IBD and the tryptophan-kynurenine metabolic pathway is overviewed in the light of recent publications.