no code implementations • 19 Jul 2022 • Xiaochun Lei, Weiliang Mai, Junlin Xie, He Liu, Zetao Jiang, Zhaoting Gong, Chang Lu, Linjun Lu
The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness.
no code implementations • 10 May 2022 • Xiaochun Lei, Linjun Lu, Zetao Jiang, Zhaoting Gong, Chang Lu, Jiaming Liang
Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilization of multiscale features.
no code implementations • 10 May 2022 • Xiaochun Lei, Chang Lu, Zetao Jiang, Zhaoting Gong, Xiang Cai, Linjun Lu
Deep neural networks (DNNs) are vulnerable to adversarial attacks.
1 code implementation • 10 Apr 2022 • Chang Lu, Chandan K. Reddy, Ping Wang, Dong Nie, Yue Ning
In this work, we propose a Multi-label Time-series GAN (MTGAN) to generate EHR and simultaneously improve the quality of uncommon disease generation.
1 code implementation • 9 Dec 2021 • Chang Lu, Tian Han, Yue Ning
We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes.
1 code implementation • 9 Jun 2021 • Chang Lu, Chandan K. Reddy, Yue Ning
Electronic Health Records (EHR) have been heavily used in modern healthcare systems for recording patients' admission information to hospitals.
1 code implementation • 16 May 2021 • Chang Lu, Chandan K. Reddy, Prithwish Chakraborty, Samantha Kleinberg, Yue Ning
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients.
no code implementations • 17 Jun 2020 • Wenjie Jiang, Chang Lu, Jing Qu, Xiaoyu Mei
We constructed a 4-lncRNA model to predict the prognosis of patients with SKCM, indicating that these lncRNAs may play a unique role in the carcinogenesis of SKCM.
no code implementations • 15 Sep 2017 • Yanyun Qu, Li Lin, Fumin Shen, Chang Lu, Yang Wu, Yuan Xie, DaCheng Tao
We propose a novel image classification method based on learning hierarchical inter-class structures.