Search Results for author: Jiawen Yao

Found 8 papers, 1 papers with code

Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

1 code implementation23 Sep 2020 Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, Junzhou Huang

We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.

Deep Attention Multiple Instance Learning +2

DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging

no code implementations26 Aug 2020 Jiawen Yao, Yu Shi, Le Lu, Jing Xiao, Ling Zhang

We present a multi-task CNN to accomplish both tasks of outcome and margin prediction where the network benefits from learning the tumor resection margin related features to improve survival prediction.

Survival Analysis Survival Prediction

CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

no code implementations5 Sep 2019 Bo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu

This is the focus of our work, where we present a principled data curation tool to extract multi-phase CT liver studies and identify each scan's phase from a real-world and heterogenous hospital PACS dataset.

Robust Contextual Bandit via the Capped-$\ell_{2}$ norm

no code implementations17 Aug 2017 Feiyun Zhu, Xinliang Zhu, Sheng Wang, Jiawen Yao, Junzhou Huang

In the critic updating, the capped-$\ell_{2}$ norm is used to measure the approximation error, which prevents outliers from dominating our objective.

Decision Making

WSISA: Making Survival Prediction From Whole Slide Histopathological Images

no code implementations CVPR 2017 Xinliang Zhu, Jiawen Yao, Feiyun Zhu, Junzhou Huang

Different from existing state-of-the-arts image-based survival models which extract features using some patches from small regions of WSIs, the proposed framework can efficiently exploit and utilize all discriminative patterns in WSIs to predict patients' survival status.

Survival Analysis Survival Prediction

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