J
Jiawen Yao
Researcher at University of Texas at Arlington
Publications - 53
Citations - 1727
Jiawen Yao is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Computer science & Cancer. The author has an hindex of 16, co-authored 39 publications receiving 939 citations. Previous affiliations of Jiawen Yao include Xi'an Jiaotong University & Tencent.
Papers
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Proceedings ArticleDOI
WSISA: Making Survival Prediction from Whole Slide Histopathological Images
TL;DR: This paper proposes an effective Whole Slide Histopathological Images Survival Analysis framework (WSISA) that can efficiently exploit and utilize all discriminative patterns in WSIs to predict patients survival status and applies it to the survival predictions of glioma and non-small-cell lung cancer.
Journal ArticleDOI
Background Subtraction Based on Low-Rank and Structured Sparse Decomposition
TL;DR: This work introduces a class of structured sparsity-inducing norms to model moving objects in videos and proposes a saliency measurement to dynamically estimate the support of the foreground.
Journal ArticleDOI
Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks
TL;DR: This work proposes Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling to efficiently learn imaging features from the WSI and then aggregate WSI-level information to patient-level.
Proceedings ArticleDOI
Deep convolutional neural network for survival analysis with pathological images
TL;DR: From the extensive experiments on the National Lung Screening Trial (NLST) lung cancer data, it is shown that the proposed DeepConvSurv model improves significantly compared with four state-of-the-art methods.
Book ChapterDOI
Graph CNN for Survival Analysis on Whole Slide Pathological Images
Ruoyu Li,Ruoyu Li,Jiawen Yao,Jiawen Yao,Xinliang Zhu,Xinliang Zhu,Yeqing Li,Yeqing Li,Junzhou Huang,Junzhou Huang +9 more
TL;DR: This work proposes to model WSI as graph and then develop a graph convolutional neural network (graph CNN) with attention learning that better serves the survival prediction by rendering the optimal graph representations of WSIs.