  Automated delineation of COVID-19 lesions from lung CT scans aids the
diagnosis and prognosis for patients. The asymmetric shapes and positioning of
the infected regions make the task extremely difficult. Capturing information
at multiple scales will assist in deciphering features, at global and local
levels, to encompass lesions of variable size and texture. We introduce the
Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient
segmentation of corona-infected lung areas in CT images. The model considers
activation responses from all levels of the encoding path, encompassing
multi-scalar features acquired at different levels of the network. This helps
segment target regions (lesions) of varying shape, size and contrast.
Incorporation of the entire gamut of multi-scalar characteristics into the
novel attention mechanism helps prioritize the selection of activation
responses and locations containing useful information. Determining robust and
discriminatory features along the decoder path is facilitated with deep
supervision. Connections in the decoder arm are remodeled to handle the issue
of vanishing gradient. As observed from the experimental results, FuDSA-Net
surpasses other state-of-the-art architectures; especially, when it comes to
characterizing complicated geometries of the lesions.
