Author name cluster

Xin Jia

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

EAAI Journal 2022 Journal Article

Detection of local and clustered outliers based on the density–distance decision graph

Kangsheng Li
Xin Gao
Xin Jia
Bing Xue
Shiyuan Fu
Zhiyu Liu
Xu Huang
Zijian Huang

Outlier detection tasks refer to identifying the objects that have different characteristics from the normal observations. Most existing approaches detect outliers from the global perspective, which can effectively detect global outliers and most clustered outliers but cannot detect local outliers when the normal samples form clusters with different densities. The methods based on local outlier factors can effectively detect local outliers, but when the number of outliers increases, the more occurrences of clustered outliers will lead to the degeneration of the detection performance. We proposed an outlier detection method based on density–distance decision graph to detect local, global and clustered outliers simultaneously. Firstly, kernel density estimation and local reachable distance are combined to calculate the local density. The density ratio of the neighbors of an instance to itself is calculated as the degree of local outliers. Then, we propose a metric named density lifting distance as the degree of global outliers, which is calculated by the distance between k nearest neighbors with higher density of the instance and itself. The density ratio and density lift distance are combined to draw the density–distance decision graph, and the product of two metrics is calculated as the final outlier score. Comprehensive experiments were conducted on 8 synthetic datasets and 16 real-world datasets compared with 12 state-of-the-art methods. The results show that the proposed method works well when the samples form clusters with different densities as well as the percentage of outliers varies, and outperforms the state-of-the-art methods tested in terms of AUC.

Details DOI

JBHI Journal 2022 Journal Article

Ultrasound Entropy Imaging for Detection and Monitoring of Thermal Lesion During Microwave Ablation of Liver

Xiejing Li
Xin Jia
Ting Shen
Mengke Wang
Guang Yang
Hua Wang
Qinli Sun
Mingxi Wan

Ultrasonic B-mode imaging offers non-invasive and real-time monitoring of thermal ablation treatment in clinical use, however it faces challenges of moderate lesion-normal contrast and detection accuracy. Quantitative ultrasound imaging techniques have been proposed as promising tools to evaluate the microstructure of ablated tissue. In this study, we introduced Shannon entropy, a non-model based statistical measurement of disorder, to quantitatively detect and monitor microwave-induced ablation in porcine livers. Performance of typical Shannon entropy (TSE), weighted Shannon entropy (WSE), and horizontally normalized Shannon entropy (hNSE) were explored and compared with conventional B-mode imaging. TSE estimated from non-normalized probability distribution histograms was found to have insufficient discernibility of different disorder of data. WSE that improves from TSE by adding signal amplitudes as weights obtained area under receiver operating characteristic (AUROC) curve of 0. 895, whereas it underestimated the periphery of lesion region. hNSE provided superior ablated area prediction with the correlation coefficient of 0. 90 against ground truth, AUROC of 0. 868, and remarkable lesion-normal contrast with contrast-to-noise ratio of 5. 86 which was significantly higher than other imaging methods. Data distributions shown in horizontally normalized probability distribution histograms indicated that the disorder of backscattered envelope signal from ablated region increased as treatment went on. These findings suggest that hNSE imaging could be a promising technique to assist ultrasound guided percutaneous thermal ablation.

Details DOI

AAAI Conference 2021 Conference Paper

EQG-RACE: Examination-Type Question Generation

Xin Jia
Wenjie Zhou
Xu Sun
Yunfang Wu

Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments. However, existing QG technologies encounter several key issues concerning the biased and unnatural language sources of datasets which are mainly obtained from the Web (e. g. SQuAD). In this paper, we propose an innovative Examination-type Question Generation approach (EQG- RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. A Rough Answer and Key Sentence Tagging scheme is utilized to enhance the representations of input. An Answer-guided Graph Convolutional Network (AG-GCN) is designed to capture structure information in revealing the inter-sentences and intra-sentence relations. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines. In addition, our work has established a new QG prototype with a reshaped dataset and QG method, which provides an important benchmark for related research in future work. We will make our data and code publicly available for further research.

PDF Details

JBHI Journal 2020 Journal Article

Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks

Siyuan Zhang
Shan Wu
Shaoqiang Shang
Xuewei Qin
Xin Jia
Dapeng Li
Zhiwei Cui
Tianqi Xu

Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the low intrinsic contrast between the thermal lesion and normal tissue. Deep learning, especially convolutional neural network (CNN), has shown significant improvements in medical image analysis. Here, we propose and evaluate an US imaging based on a CNN architecture for the detection and monitoring of thermal lesions induced by MWA in porcine livers. Unlike dealing with images in many visual object recognition tasks, US radiofrequency (RF) data backscattered from the ablated region were utilized to capture features related to the thermal lesion. The dataset comprised of 1640 US RF envelope data matrices and their corresponding gross-pathology images, and were utilized for training and testing. After envelope detection, US B-mode, segmentation results based on CNN (SI CNN ), and modified CNN (SI m-CNN ) for US data were simultaneously reconstructed to reveal the suitability for monitoring of MWA. The SI CNN and SI m-CNN outperformed B-mode images for the detection and monitoring of MWA-induced thermal lesions. The values of the area under the receiver operating characteristic curve were 0. 8728 and 0. 8948 for the SI CNN and Si m-CNN, respectively, which were both higher than the value of 0. 6904 for B-mode images. Ablated regions that were assessed using SI m-CNN showed a good correlation (J 0. 8845, r 0. 8739, and E 0. 410) to gross-pathology images. This study was the first to illustrate that SI m-CNN has the potential to detect and monitor thermal lesions, and may be utilized as an alternative modality for image-guided MWA treatments.

Details DOI