Search Results for author:
Found 6 papers, 1 papers with code
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network
Conclusions: The experimental result demonstrates the effectiveness of the proposed MM-SFENet on the localization and classification of bladder cancer.
BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition
The LCE module utilizes a graph to model the global co-occurrence relationship between multiple labels and employs graph convolutional networks for learning inference.
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch.
NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement
Then a minimum fusion strategy on the results of these two transforms is utilized to achieve more natural illumination component enhancement.
Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation
In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation.
Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning
However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive.
