Search Results for author: Si-Yuan Zhang

Found 7 papers, 2 papers with code

Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation

1 code implementation9 Mar 2021 Navdeep Dahiya, Sadegh R Alam, Pengpeng Zhang, Si-Yuan Zhang, Anthony Yezzi, Saad Nadeem

Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures.

Data Augmentation Translation

A feature-supervised generative adversarial network for environmental monitoring during hazy days

no code implementations5 Aug 2020 Ke Wang, Si-Yuan Zhang, Junlan Chen, Fan Ren, Lei Xiao

First, pairs of hazy and clean images are used as inputs to supervise the encoding process and obtain high-quality feature maps.

Generative Adversarial Network

Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation

no code implementations28 Jun 2020 Sadegh R Alam, Tianfang Li, Pengpeng Zhang, Si-Yuan Zhang, Saad Nadeem

Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis.

Data Augmentation Domain Adaptation

Joint Embedding in Named Entity Linking on Sentence Level

no code implementations12 Feb 2020 Wei Shi, Si-Yuan Zhang, Zhiwei Zhang, Hong Cheng, Jeffrey Xu Yu

The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document.

Entity Linking Knowledge Graphs +1

A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images

4 code implementations CVPR 2019 Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Si-Yuan Zhang, Cody Smith, Scott Howard

In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising.


Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation

no code implementations28 Jun 2018 Zhuo Zhao, Lin Yang, Hao Zheng, Ian H. Guldner, Si-Yuan Zhang, Danny Z. Chen

Our approach needs only 3D bounding boxes for all instances and full voxel annotation for a small fraction of the instances, and uses a novel two-stage 3D instance segmentation model utilizing these two kinds of annotation, respectively.

3D Instance Segmentation Segmentation +1

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