Search Results for author: Li Xiao

Found 32 papers, 8 papers with code

Constructing Uyghur Name Entity Recognition System using Neural Machine Translation Tag Projection

no code implementations CCL 2020 Anwar Azmat, Li Xiao, Yang Yating, Dong Rui, Osman Turghun

Although named entity recognition achieved great success by introducing the neural networks, it is challenging to apply these models to low resource languages including Uyghur while it depends on a large amount of annotated training data.

Machine Translation named-entity-recognition +4

A Multiple Market Trading Mechanism for Electricity, Renewable Energy Certificate and Carbon Emission Right of Virtual Power Plants

no code implementations8 Aug 2022 Zhihong Huang, Ye Guo, Qiuwei Wu, Li Xiao, Hongbin Sun

With the introduction of the inventory mechanism of REC and CER, the profit of the VPP increases and better trading decisions with multiple markets are made under the requirements of renewable portfolio standard (RPS) and carbon emission (CE) quota requirements.

SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech

no code implementations28 May 2022 Hanqing Guo, Qiben Yan, Nikolay Ivanov, Ying Zhu, Li Xiao, Eric J. Hunter

Our evaluation shows that SUPERVOICE achieves 0. 58% equal error rate in the speaker verification task, it only takes 120 ms for testing an incoming utterance, outperforming all existing speaker verification systems.

Text-Independent Speaker Verification

CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation

no code implementations6 Apr 2022 Mingyang Liu, Li Xiao, Huiqin Jiang, Qing He

In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions.

Lesion Segmentation

i-Razor: A Neural Input Razor for Feature Selection and Dimension Search in Large-Scale Recommender Systems

no code implementations1 Apr 2022 Yao Yao, Bin Liu, Haoxun He, Dakui Sheng, Ke Wang, Li Xiao, Huanhuan Cao

Typically, feature selection and embedding dimension search are optimized sequentially, i. e., feature selection is performed first, followed by embedding dimension search to determine the optimal dimension size for each selected feature.

Click-Through Rate Prediction Feature Engineering +3

Learning Incrementally to Segment Multiple Organs in a CT Image

no code implementations4 Mar 2022 Pengbo Liu, Xia Wang, Mengsi Fan, Hongli Pan, Minmin Yin, Xiaohong Zhu, Dandan Du, Xiaoying Zhao, Li Xiao, Lian Ding, Xingwang Wu, S. Kevin Zhou

In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.

Incremental Learning

CLS: Cross Labeling Supervision for Semi-Supervised Learning

no code implementations17 Feb 2022 Yao Yao, Junyi Shen, Jin Xu, Bin Zhong, Li Xiao

Based on FixMatch, where a pseudo label is generated from a weakly-augmented sample to teach the prediction on a strong augmentation of the same input sample, CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning.

pseudo label

Knowledge Matters: Radiology Report Generation with General and Specific Knowledge

no code implementations30 Dec 2021 Shuxin Yang, Xian Wu, Shen Ge, Shaohua Kevin Zhou, Li Xiao

In this paper, we propose a knowledge-enhanced radiology report generation approach introduces two types of medical knowledge: 1) General knowledge, which is input independent and provides the broad knowledge for report generation; 2) Specific knowledge, which is input dependent and provides the fine-grained knowledge for report generation.

General Knowledge Image Captioning

IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control

no code implementations6 Jul 2021 Xiaoyan Cao, Yao Yao, Lanqing Li, Wanpeng Zhang, Zhicheng An, Zhong Zhang, Li Xiao, Shihui Guo, Xiaoyu Cao, Meihong Wu, Dijun Luo

However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task.

Decision Making

Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation

1 code implementation5 Jul 2021 Yao Yao, Li Xiao, Zhicheng An, Wanpeng Zhang, Dijun Luo

Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics.

Continuous Control reinforcement-learning

AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction

no code implementations16 Jun 2021 Hao Chen, Fuzhen Zhuang, Li Xiao, Ling Ma, Haiyan Liu, Ruifang Zhang, Huiqin Jiang, Qing He

The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information.

Disease Prediction text similarity

Pathological Image Segmentation with Noisy Labels

no code implementations20 Mar 2021 Li Xiao, Yinhao Li, Luxi Qv, Xinxia Tian, Yijie Peng, S. Kevin Zhou

Segmentation of pathological images is essential for accurate disease diagnosis.

Semantic Segmentation

Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets

no code implementations8 Mar 2021 Pengbo Liu, Li Xiao, S. Kevin Zhou

In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.

Incremental Learning

One-Shot Medical Landmark Detection

2 code implementations8 Mar 2021 Qingsong Yao, Quan Quan, Li Xiao, S. Kevin Zhou

The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images.

Self-Supervised Learning

You Only Learn Once: Universal Anatomical Landmark Detection

2 code implementations8 Mar 2021 Heqin Zhu, Qingsong Yao, Li Xiao, S. Kevin Zhou

However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region.

Anatomy

Noise Optimization for Artificial Neural Networks

1 code implementation6 Feb 2021 Li Xiao, Zeliang Zhang, Yijie Peng

Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work.

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction

no code implementations20 Jan 2021 Gang Qu, Li Xiao, Wenxing Hu, Kun Zhang, Vince D. Calhoun, Yu-Ping Wang

Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.

Graph Embedding Time Series

Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models

1 code implementation16 Dec 2020 Pengbo Liu, Hu Han, Yuanqi Du, Heqin Zhu, Yinhao Li, Feng Gu, Honghu Xiao, Jun Li, Chunpeng Zhao, Li Xiao, Xinbao Wu, S. Kevin Zhou

Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.

G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object Detection

no code implementations14 Nov 2020 Yufan Luo, Li Xiao

By comparing the performance between the original Faster R-CNN and that with partially separated feature maps, we show that: (1) Sharing high-level features for the classification and localization tasks is sub-optimal; (2) Large stride is beneficial for classification but harmful for localization; (3) Global context information could improve the performance of classification.

Classification General Classification +3

Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies

no code implementations30 Sep 2020 Li Xiao, Biao Cai, Gang Qu, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.

Connectivity Estimation Multi-Task Learning

Label-Free Segmentation of COVID-19 Lesions in Lung CT

no code implementations8 Sep 2020 Qingsong Yao, Li Xiao, Peihang Liu, S. Kevin Zhou

Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans.

COVID-19 Diagnosis Unsupervised Anomaly Detection

Marginal loss and exclusion loss for partially supervised multi-organ segmentation

no code implementations8 Jul 2020 Gonglei Shi, Li Xiao, Yang Chen, S. Kevin Zhou

Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs.

PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy

no code implementations10 Mar 2020 Li Xiao, Yufan Luo, Chunlong Luo, Lianhe Zhao, Quanshui Fu, Guoqing Yang, Anpeng Huang, Yi Zhao

Based on these principles, we designed a novel boundary refinement architecture to improve localization accuracy by combining coarse-to-fine framework with feature pyramid structure, named as Pyramidal Bounding Box Refinement network(PBRnet), which parameterizes gradually focused boundary areas of objects and leverages lower-level feature maps to extract finer local information when refining the predicted bounding boxes.

Object Localization

Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography

no code implementations1 Mar 2020 Li Xiao, Cheng Zhu, Junjun Liu, Chunlong Luo, Peifang Liu, Yi Zhao

It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists.

Breast Cancer Detection object-detection +1

Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness

1 code implementation31 Jan 2019 Li Xiao, Yijie Peng, Jeff Hong, Zewu Ke, Shuhuai Yang

In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,. e. g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions.

General Classification

Radial Velocity Retrieval for Multichannel SAR Moving Targets with Time-Space Doppler De-ambiguity

no code implementations1 Oct 2016 Jia Xu, Zu-Zhen Huang, Zhi-Rui Wang, Li Xiao, Xiang-Gen Xia, Teng Long

Accordingly, the multichannel SAR systems with different parameters are investigated in three different cases with diverse Doppler ambiguity properties, and a multi-frequency SAR is then proposed to obtain the RV estimation by solving the ambiguity problem based on Chinese remainder theorem (CRT).

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