no code implementations • 2 Mar 2023 • Gaochao Song, Luo Zhang, Ran Su, Jianfeng Shi, Ying He, Qian Sun
Motivated by position encoding, we propose orthogonal position encoding (OPE) - an extension of position encoding - and an OPE-Upscale module to replace the INR-based upsampling module for arbitrary-scale image super-resolution.
no code implementations • 3 Dec 2022 • Chao Pang, Yu Wang, Yi Jiang, Ruheng Wang, Ran Su, Leyi Wei
Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs.
1 code implementation • 11 Oct 2022 • Tianling Liu, Ran Su, Changming Sun, Xiuting Li, Leyi Wei
Next, we developed a survival prediction model, named DeepConvAttentionSurv (DCAS), which was able to extract patch-level features, removed less discriminative clusters and predicted the EOC survival precisely.
1 code implementation • 20 Apr 2021 • Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Leyi Wei, Ran Su
DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine segmentation results.
1 code implementation • 20 Apr 2021 • Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Ran Su
The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function.
1 code implementation • 4 Nov 2018 • Qiangguo Jin, Zhaopeng Meng, Changming Sun, Leyi Wei, Ran Su
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes.
no code implementations • 3 Nov 2018 • Qiangguo Jin, Zhaopeng Meng, Tuan D. Pham, Qi Chen, Leyi Wei, Ran Su
Results show that more detailed vessels are extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0. 9697/0. 9722/0. 9724 and AUC of 0. 9856/0. 9868/0. 9863 on DRIVE, STARE and CHASE_DB1 respectively.
Ranked #5 on Retinal Vessel Segmentation on STARE