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学者姓名：贾熹滨
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摘要 ：
Cross-domain sentiment analysis (CDSA) aims to learn transferable knowledge from the source domain to facilitate the sentiment polarity classification on the target domain of lacking labeled data. Currently, two types of unsupervised domain adaptation (UDA) methods are widely used in CDSA tasks. One employs the domain adversarial strategy to extract domain-invariant features, and the other utilizes the distance metric strategy to reduce domain distribution discrepancy. However, the fine-grained domain-specific information related to categories aligned between domains is not preserved, which suppresses the performance of target-domain classification. To overcome the mentioned problem, a unified Domain Adversarial Category-wise Alignment Network (DACAN) was proposed in this paper. An integrated network was constructed with progressive multi-level feature learning. Specifically, a feature extraction module was constructed with parameter sharing between two domains at low-level text feature extraction layers. The domain adversarial module was added to enable shared knowledge transfer by extracting domain-invariant information and by updating the shared parameters at the feature extraction layers. A category-wise alignment module was built to achieve local distribution alignment at the high dimension-level semantic layers guided by fine-grained category structure information. Meanwhile, joint constraint was established with domain-invariant constraint based on domain adversarial, and domain-consistency constraint based on category-wise alignment. Comprehensive experiments were conducted on two standard Amazon review datasets. The results show that DACAN outperforms other state-of-the-art UDA methods by 0.7% and 1.1% on the two-and three-category CDSA tasks, respectively. Also, better performance results are achieved with a synergistic UDA scheme compared with a single UDA scheme.
关键词 ：
Progressive multi-level feature learning Progressive multi-level feature learning Cross-domain sentiment classification Cross-domain sentiment classification Category-wise alignment Category-wise alignment Unsupervised domain adaptation Unsupervised domain adaptation Domain adversarial Domain adversarial
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| GB/T 7714 | Jia, Xibin , Li, Chen , Zeng, Meng et al. An improved unified domain adversarial category-wise alignment network for unsupervised cross-domain sentiment classification [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
| MLA | Jia, Xibin et al. "An improved unified domain adversarial category-wise alignment network for unsupervised cross-domain sentiment classification" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 126 (2023) . |
| APA | Jia, Xibin , Li, Chen , Zeng, Meng , Wang, Luo , Mi, Qing . An improved unified domain adversarial category-wise alignment network for unsupervised cross-domain sentiment classification . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
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摘要 ：
Hepatocellular carcinoma (HCC) is a type of primary liver malignant tumor with a high recurrence rate and poor prognosis even undergoing resection or transplantation. Accurate discrimination of the histologic grades of HCC plays a critical role in the management and therapy of HCC patients. In this paper, we discuss a deep learning-based diagnostic model for HCC histologic grading with multimodal Magnetic Resonance Imaging (MRI) images to overcome the problem of limited well-annotated data and extract the discriminated fusion feature referring to the clinical diagnosis experience of radiologists. Accordingly, we propose a novel Multimodality-Contribution-Aware TripNet (MCAT) based on the metric learning and the attention-aware weighted multimodal fusion. The novelty of the method lies in the multimodality small-shot learning architecture designation and the multimodality adaptive weighted computing scheme. The comprehensive experiments are done on the clinic dataset with the well-annotation of lesion location by the professional radiologist. The experimental results show that our proposed MCAT is not only able to achieve acceptable quantitative measuring of HCC histologic grading based on the MRI sequences with small cases but also outperforms previous models in HCC histologic grading, reaching an accuracy of 84 percent, a sensitivity of 87 percent and precision of 89 percent.
关键词 ：
multimodality-contribution-aware attention weighting multimodality-contribution-aware attention weighting Lesions Lesions Medical diagnostic imaging Medical diagnostic imaging Tumors Tumors histologic grading of hepatocellular carcinoma histologic grading of hepatocellular carcinoma Feature extraction Feature extraction Task analysis Task analysis small-shot learning small-shot learning multimodality fusion multimodality fusion Training Training Noninvasive diagnosis Noninvasive diagnosis Magnetic resonance imaging Magnetic resonance imaging
引用：
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| GB/T 7714 | Jia, Xibin , Sun, Zheng , Mi, Qing et al. A Multimodality-Contribution-Aware TripNet for Histologic Grading of Hepatocellular Carcinoma [J]. | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS , 2022 , 19 (4) : 2003-2016 . |
| MLA | Jia, Xibin et al. "A Multimodality-Contribution-Aware TripNet for Histologic Grading of Hepatocellular Carcinoma" . | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 19 . 4 (2022) : 2003-2016 . |
| APA | Jia, Xibin , Sun, Zheng , Mi, Qing , Yang, Zhenghan , Yang, Dawei . A Multimodality-Contribution-Aware TripNet for Histologic Grading of Hepatocellular Carcinoma . | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS , 2022 , 19 (4) , 2003-2016 . |
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摘要 ：
金字塔原型对齐的轻量级小样本语义分割网络
关键词 ：
原型对齐正则化 原型对齐正则化 卷积神经网络 卷积神经网络 轻量级网络 轻量级网络 小样本语义分割 小样本语义分割 多尺度 多尺度 金字塔池化 金字塔池化
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| GB/T 7714 | 贾熹滨 , 李佳 , 北京工业大学学报 . 金字塔原型对齐的轻量级小样本语义分割网络 [J]. | 贾熹滨 , 2021 , 47 (5) : 455-462,519 . |
| MLA | 贾熹滨 et al. "金字塔原型对齐的轻量级小样本语义分割网络" . | 贾熹滨 47 . 5 (2021) : 455-462,519 . |
| APA | 贾熹滨 , 李佳 , 北京工业大学学报 . 金字塔原型对齐的轻量级小样本语义分割网络 . | 贾熹滨 , 2021 , 47 (5) , 455-462,519 . |
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摘要 ：
One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.
关键词 ：
calcium detection calcium detection coronary artery calcium score CT coronary artery calcium score CT deep learning deep learning image classification image classification inception resnet V2 inception resnet V2 resnet-50 resnet-50 VGG VGG
引用：
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| GB/T 7714 | Lee, Sungjin , Rim, Beanbonyka , Jou, Sung-Shick et al. Deep-Learning-Based Coronary Artery Calcium Detection from CT Image [J]. | SENSORS , 2021 , 21 (21) . |
| MLA | Lee, Sungjin et al. "Deep-Learning-Based Coronary Artery Calcium Detection from CT Image" . | SENSORS 21 . 21 (2021) . |
| APA | Lee, Sungjin , Rim, Beanbonyka , Jou, Sung-Shick , Gil, Hyo-Wook , Jia, Xibin , Lee, Ahyoung et al. Deep-Learning-Based Coronary Artery Calcium Detection from CT Image . | SENSORS , 2021 , 21 (21) . |
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| GB/T 7714 | Kim, Soo Kyun , Choi, Min-Hyung , Chun, Junchul et al. Real-time 2D/ 3D image processing with deep learning [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2021 , 80 (28-29) : 35771-35771 . |
| MLA | Kim, Soo Kyun et al. "Real-time 2D/ 3D image processing with deep learning" . | MULTIMEDIA TOOLS AND APPLICATIONS 80 . 28-29 (2021) : 35771-35771 . |
| APA | Kim, Soo Kyun , Choi, Min-Hyung , Chun, Junchul , Jia, Xibin . Real-time 2D/ 3D image processing with deep learning . | MULTIMEDIA TOOLS AND APPLICATIONS , 2021 , 80 (28-29) , 35771-35771 . |
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摘要 ：
Unsupervised clustering is a kind of popular solution for unsupervised person re-identification (re-ID). However, due to the influence of cross-view differences, the results of clustering labels are not accurate. To solve this problem, an unsupervised re ID method based on cross-view distributed alignment (CV-DA) to reduce the influence of unsupervised cross-view is proposed. Specifically, based on a popular unsupervised clustering method, density clustering DBSCAN is used to obtain pseudo labels. By calculating the similarity scores of images in the target domain and the source domain, the similarity distribution of different camera views is obtained and is aligned with the distribution with the consistency constraint of pseudo labels. The cross-view distribution alignment constraint is used to guide the clustering process to obtain a more reliable pseudo label. The comprehensive comparative experiments are done in two public datasets, i.e. Market-1501 and DukeMTMC-reID. The comparative results show that the proposed method outperforms several state-of-the-art approaches with mAP reaching 52.6% and rank1 71.1%. In order to prove the effectiveness of the proposed CV-DA, the proposed constraint is added into two advanced re-ID methods. The experimental results demonstrate that the mAP and rank increase by ?0.5-2% after using the cross-view distribution alignment constraint comparing with that of the associated original methods without using CV-DA.
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| GB/T 7714 | Jia, Xibin , Wang, Xing , Mi, Qing . An unsupervised person re-identification approach based on cross-view distribution alignment [J]. | IET IMAGE PROCESSING , 2021 , 15 (11) : 2693-2704 . |
| MLA | Jia, Xibin et al. "An unsupervised person re-identification approach based on cross-view distribution alignment" . | IET IMAGE PROCESSING 15 . 11 (2021) : 2693-2704 . |
| APA | Jia, Xibin , Wang, Xing , Mi, Qing . An unsupervised person re-identification approach based on cross-view distribution alignment . | IET IMAGE PROCESSING , 2021 , 15 (11) , 2693-2704 . |
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摘要 ：
Context: Training deep learning models for code readability classification requires large datasets of quality pre-labeled data. However, it is almost always time-consuming and expensive to acquire readability data with manual labels. Objective: We thus propose to introduce data augmentation approaches to artificially increase the size of training set, this is to reduce the risk of overfitting caused by the lack of readability data and further improve the classification accuracy as the ultimate goal. Method: We create transformed versions of code snippets by manipulating original data from aspects such as comments, indentations, and names of classes/methods/variables based on domain-specific knowledge. In addition to basic transformations, we also explore the use of Auxiliary Classifier GANs to produce synthetic data. Results: To evaluate the proposed approach, we conduct a set of experiments. The results show that the classification performance of deep neural networks can be significantly improved when they are trained on the augmented corpus, achieving a state-of-the-art accuracy of 87.38%. Conclusion: We consider the findings of this study as primary evidence of the effectiveness of data augmentation in the field of code readability classification.
关键词 ：
Generative adversarial network Generative adversarial network Deep learning Deep learning Data augmentation Data augmentation Empirical software engineering Empirical software engineering Code readability classification Code readability classification
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| GB/T 7714 | Mi, Qing , Xiao, Yan , Cai, Zhi et al. The effectiveness of data augmentation in code readability classification [J]. | INFORMATION AND SOFTWARE TECHNOLOGY , 2021 , 129 . |
| MLA | Mi, Qing et al. "The effectiveness of data augmentation in code readability classification" . | INFORMATION AND SOFTWARE TECHNOLOGY 129 (2021) . |
| APA | Mi, Qing , Xiao, Yan , Cai, Zhi , Jia, Xibin . The effectiveness of data augmentation in code readability classification . | INFORMATION AND SOFTWARE TECHNOLOGY , 2021 , 129 . |
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摘要 ：
本发明公开了基于知识图谱的问答系统的关系检测方法，属于自然语言智能问答技术领域；本发明考虑到问句意图对于答案的选择具有影响，提出加入问句意图辅助检测关系的方法，并在匹配知识图谱信息与自然语言问句时，提出了一种双向注意力机制的Bi‑GRU编码算法，考虑知识图谱中关系信息对问句中每个单词的关注程度差异的同时，考虑计算问句对关系信息的注意力，利用问句的不同表示与关系不同信息的注意力加权来计算问句与知识图谱关系匹配的相似度。改进后的双向注意力关系检测模型，有效提升了对知识图谱中不同候选关系与问题相似度的动态自适应加权，充分利用全局语义信息的同时关注重点信息，从而检索出问句关联度更高的答案。
引用：
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| GB/T 7714 | 贾熹滨 , 刘洋 . 基于知识图谱的问答系统的关系检测方法 : CN202010193257.9[P]. | 2020-03-18 . |
| MLA | 贾熹滨 et al. "基于知识图谱的问答系统的关系检测方法" : CN202010193257.9. | 2020-03-18 . |
| APA | 贾熹滨 , 刘洋 . 基于知识图谱的问答系统的关系检测方法 : CN202010193257.9. | 2020-03-18 . |
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摘要 ：
本发明公开了一种基于多模态影像贡献度融合的肝细胞癌分化评估方法，首先，建立有效高维多模态影像数据的关联表示，即选择一种合适的特征提取方式对多模态MRI影像进行特征提取，同时，利用多模态MRI影像贡献度自适应加权机制，对九个模态的MRI影像进行任务贡献度学习，然后将任务贡献度学习所得的参数结果与多模态融合MRI数据经过特征提取器所得的特征进行特征层融合，最后，在网络顶端添加分类器，使用结合了贡献度的多模态MRI影像特征进行HCC分化程度的分级任务，以实现更为精准的预测。比起传统影像学诊断方法，本发明排除了主观因素的影响并同时考虑到了各个多模态MRI序列的诊断能力和贡献，从而使得到的结果更加准确和鲁棒。
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| GB/T 7714 | 贾熹滨 , 孙政 , 杨正汉 et al. 一种基于多模态影像贡献度融合的肝细胞癌分化评估方法 : CN202010405639.3[P]. | 2020-05-14 . |
| MLA | 贾熹滨 et al. "一种基于多模态影像贡献度融合的肝细胞癌分化评估方法" : CN202010405639.3. | 2020-05-14 . |
| APA | 贾熹滨 , 孙政 , 杨正汉 , 杨大为 . 一种基于多模态影像贡献度融合的肝细胞癌分化评估方法 : CN202010405639.3. | 2020-05-14 . |
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摘要 ：
本发明公开了基于生成对抗网络为医疗影像数据集做数据增广的GAN网络架构及方法，包括：获取现有医疗影像的真实数据集；在样本中，取出含病灶的样本与不含病灶的样本，作为一组一起输入，运行一个循环生成式对抗网络，得到与真实数据相似的人工样本；将人工样本加入到真实数据集中，得到混合数据集；将混合数据集作为输入，使用分类器进行分类任务。本发明引入重建一致性损失函数约束条件，实现从源分布转换为目标分布，然后重建源分布；最后在鉴别器中增加了稳定归一化层，有效地模拟了真实数据的分布特征，通过生成对抗网络生成图像，进行数据增强，然后仿真了大量医疗影像图像样本，有效改善了数据样本不足对医疗影像数据分类任务造成的影响。
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| GB/T 7714 | 贾熹滨 , 毕光耀 . 基于生成对抗网络为医疗影像数据集做数据增广的GAN网络架构及方法 : CN202010304146.0[P]. | 2020-04-17 . |
| MLA | 贾熹滨 et al. "基于生成对抗网络为医疗影像数据集做数据增广的GAN网络架构及方法" : CN202010304146.0. | 2020-04-17 . |
| APA | 贾熹滨 , 毕光耀 . 基于生成对抗网络为医疗影像数据集做数据增广的GAN网络架构及方法 : CN202010304146.0. | 2020-04-17 . |
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