5 citations · 8 across the 7 of their papers we have counts for
7 papers
VPBSD:Vessel-Pattern-Based Semi-Supervised Distillation for Efficient 3D Microscopic Cerebrovascular Segmentation
Xi Lin, Shixuan Zhao, Xinxu Wei +2
3D microscopic cerebrovascular images are characterized by their high resolution, presenting significant annotation challenges, large data volumes, and intricate variations in deta…
Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning
Xinxu Wei, Xi Lin, Haiyun Liu +2
Effective retinal vessel segmentation requires a sophisticated integration of global contextual awareness and local vessel continuity. To address this challenge, we propose the Gra…
Orientation and Context Entangled Network for Retinal Vessel Segmentation
Xinxu Wei, Kaifu Yang, Danilo Bzdok +1
Most of the existing deep learning based methods for vessel segmentation neglect two important aspects of retinal vessels, one is the orientation information of vessels, and the ot…
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays
Xinxu Wei, Haohan Bai, Xianshi Zhang +1
Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia. With the emergence of deep learning, the identification accuracy of pneumonia ha…
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement
Xinxu Wei, Xianshi Zhang, Shisen Wang +2
Low-light image enhancement is a challenging low-level computer vision task because after we enhance the brightness of the image, we have to deal with amplified noise, color distor…
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement
Xinxu Wei, Xianshi Zhang, Shisen Wang +4
Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color distortion, unknown nois…
BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration
Xinxu Wei, Xianshi Zhang, Shisen Wang +4
Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color bias, unknown noise, det…