Dual-Path Attention Compensation U-Net for Stroke Lesion Segmentation.

For the segmentation task of stroke lesions, using the attention U-Net model based on the self-attention mechanism can suppress irrelevant regions in an input image while highlighting salient features useful for specific tasks. However, when the lesion is small and the lesion contour is blurred, attention U-Net may generate wrong attention coefficient maps, leading to incorrect segmentation results. To cope with this issue, we propose a dual-path attention compensation U-Net (DPAC-UNet) network, which consists of a primary network and auxiliary path network.

[Periodontal status in patients with oral submucous fibrosis].

Objective: To investigate the periodontal status in patients with oral submucous fibrosis (OSF), and to provide reference for the treatment and prophylaxis in patients with OSF and betel chewers.

Methods: Fifty samples clinically and pathologically diagnosed as OSF patients were selected as the OSF group, another 50 age-matched healthy volunteers in the similar living condition were compared with the OSF patients and non-betel nut chewers were classified as the control group. The 5 periodontal clinical parameters were collected and recorded, including plaque index, periodontal probing depth, clinical attachment loss, gingival index, and tooth count of bleeding of probing.