Search Results for author: Ran Su

Found 6 papers, 4 papers with code

Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery

no code implementations3 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.

Representation Learning

EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images

1 code implementation11 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.

Survival Analysis Survival Prediction +1

Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images

1 code implementation20 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.

Domain Adaptation

Free-form tumor synthesis in computed tomography images via richer generative adversarial network

1 code implementation20 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.

Association Computed Tomography (CT)

RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

1 code implementation4 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.

Brain Tumor Segmentation Deep Attention +2

DUNet: A deformable network for retinal vessel segmentation

no code implementations3 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.

Retinal Vessel Segmentation

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