Search Results for author: Xiaoqing Liu

Found 11 papers, 2 papers with code

Generate to Understand for Representation

1 code implementation14 Jun 2023 Changshang Xue, Xiande Zhong, Xiaoqing Liu

In recent years, a significant number of high-quality pretrained models have emerged, greatly impacting Natural Language Understanding (NLU), Natural Language Generation (NLG), and Text Representation tasks.

Contrastive Learning Language Modelling +3

Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation

no code implementations26 Apr 2023 Xiaoqing Liu, Kengo Araki, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Ryoma Bise

The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the target domain (from different hospitals) due to the different image features.

Image Segmentation Semantic Segmentation +2

Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation

no code implementations26 Apr 2023 Xiaoqing Liu, Kenji Ono, Ryoma Bise

The development of medical image segmentation using deep learning can significantly support doctors' diagnoses.

Data Augmentation Image Segmentation +3

Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System

no code implementations24 Aug 2021 Gang Yu, Zhongzhi Yu, Yemin Shi, Yingshuo Wang, Xiaoqing Liu, Zheming Li, Yonggen Zhao, Fenglei Sun, Yizhou Yu, Qiang Shu

The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage.

A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

1 code implementation21 Apr 2021 Jie Lian, Jingyu Liu, Shu Zhang, Kai Gao, Xiaoqing Liu, Dingwen Zhang, Yizhou Yu

Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.

Instance Segmentation Object Detection +1

A molecular dynamics simulation study on the frustrated Lewis pairs in ionic liquids

no code implementations21 Jan 2021 Xiaoqing Liu, XiaoJing Wang, Tianhao Yu, Weizhen Zhao, Lei Liu

Spatial distribution function (SDF) results show that toluene formed a continuum solvation shell, which hinders the interactions of (tBu)3P and B(C6F5)3 , while [Cnmim][NTf2] leave a relatively large empty space, which is accessible by (tBu3)P molecules, resulting in a higher probability of Lewis acids and bases interactions.

Chemical Physics

Adaptive noise imitation for image denoising

no code implementations30 Nov 2020 Huangxing Lin, Yihong Zhuang, Yue Huang, Xinghao Ding, Yizhou Yu, Xiaoqing Liu, John Paisley

Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.

Image Denoising

GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation

no code implementations19 Oct 2020 Zhanwei Xu, Yukun Cao, Cheng Jin, Guozhu Shao, Xiaoqing Liu, Jie zhou, Heshui Shi, Jianjiang Feng

Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients.

Image Segmentation Lesion Segmentation +2

Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity

no code implementations ACL 2020 Joseph Sirrianni, Xiaoqing Liu, Douglas Adams

This work is the first to train models for predicting a post{'}s stance polarity and intensity in one combined value in cyber argumentation with reasonably good accuracy.

regression Stance Detection

MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

no code implementations25 Dec 2018 Huai Chen, Yuxiao Qi, Yong Yin, Tengxiang Li, Xiaoqing Liu, Xiuli Li, Guanzhong Gong, Lisheng Wang

Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC.


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