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学者姓名：刘博
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Abstract ：
本发明提出了一种基于弱监督学习的超高分辨率医学图像分割方法。通过使用弱监督这一深度学习技术，可以在只有图像级别的弱标签信息的情况下，获得一个较为理想的分割效果。除此之外，本发明不仅考虑了标注信息的强弱问题，还关注到针对超高分辨率医学图像训练流程方法的改良问题，主要在医学图像数据集的前处理和模型预测时的后处理阶段提出几项改进，弥补了现有技术中模型训练流程存在的缺陷。
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| GB/T 7714 | 刘博 , 王强 , 周子安 et al. 一种基于弱监督学习的超高分辨率医学图像分割方法 : CN202310231039.3[P]. | 2023-03-13 . |
| MLA | 刘博 et al. "一种基于弱监督学习的超高分辨率医学图像分割方法" : CN202310231039.3. | 2023-03-13 . |
| APA | 刘博 , 王强 , 周子安 , 丁磊 , 杨滨 . 一种基于弱监督学习的超高分辨率医学图像分割方法 : CN202310231039.3. | 2023-03-13 . |
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本发明公开了基于PSO‑GA‑LSTM模型的空气质量预测方法，首先将序列数据进行预处理，然后利用粒子群算法优化LSTM模型超参数，从而确定LSTM模型的网络结构；利用遗传算法优化LSTM模型初始的权值阈值，确定LSTM模型的权值阈值。最后将利用最佳超参数和最佳权值阈值，建立LSTM模型，对空气质量时间序列数据进行训练并预测。本发明克服了传统的预测方法预测过程中精度不高的问题，且利用粒子群和遗传算法对LSTM参数进行优化，避免模型陷入局部最优解的问题，提高了预测收敛速度。最终实现了对空气质量时间序列的预测，更精确预测空气质量变化的趋势。
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| GB/T 7714 | 王强 , 刘博 , 朱念 et al. 基于PSO-GA-LSTM模型的空气质量预测方法 : CN202310317140.0[P]. | 2023-03-25 . |
| MLA | 王强 et al. "基于PSO-GA-LSTM模型的空气质量预测方法" : CN202310317140.0. | 2023-03-25 . |
| APA | 王强 , 刘博 , 朱念 , 李建强 , 丁磊 . 基于PSO-GA-LSTM模型的空气质量预测方法 : CN202310317140.0. | 2023-03-25 . |
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本发明公开了一种基于类激活映射的医学图像弱监督分割方法，包括下述步骤：步骤一，采集组织病理图像；步骤二，对图像进行缩放、调整和增强操作；步骤三，使用中间层的特征图对图像的病变区域进行激活；步骤四，使用一致性损失对模型进行正则处理。将网络中间层的特征图降采样到同一大小后进行拼接，用每个位置不同通道的最大值与网络最末端的特征图进行掩码；使用掩码后的特征图构建类激活映射；使用模型输出的类激活映射作为伪标签训练分割网络，实现医学图像的弱监督分割任务。本算法提出的基于类激活映射的医学图像弱监督分割方法，可以使用图像级标签对整张病理图像进行分割，为病理医生提供有效的参考价值。
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| GB/T 7714 | 刘博 , 丁磊 , 杨滨 et al. 一种基于类激活映射的医学图像弱监督分割方法 : CN202310197271.X[P]. | 2023-03-03 . |
| MLA | 刘博 et al. "一种基于类激活映射的医学图像弱监督分割方法" : CN202310197271.X. | 2023-03-03 . |
| APA | 刘博 , 丁磊 , 杨滨 , 王强 , 汪婧懿 . 一种基于类激活映射的医学图像弱监督分割方法 : CN202310197271.X. | 2023-03-03 . |
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Abstract ：
Background. Currently, predictive models were not developed based on the signaling pathway signatures of immune-related lncRNAs in breast cancer (BRCA) patients. Methods. We selected unsupervised hierarchical clustering algorithm to classify patients with BRCA based on the significant immune-derived lncRNAs from the TCGA dataset. And different methods including ESTIMATE, ImmuneCellAI, and CIBERSORT were performed to evaluate the immune infiltration of tumor microenvironment. Using Lasso regression algorithm, we filtered the significant signaling pathways enriched by GSEA, GSVA, or PPI analysis to develop a prognostic model. And a nomogram integrated with clinical factors and significant pathways was constructed to predict the precise probability of overall survival (OS) of BRCA patients in the TCGA dataset (n =1,098) and another two testing sets (n = 415). Results. BRCA patients were stratified into the PC (n = 571) and GC (n = 527) subgroup with significantly different prognosis with 550 immune-related lncRNAs in the TCGA dataset. Integrated analysis revealed different immune response, oncogenic signaling, and metabolic reprograming pathways between these two subgroups. And a 5-pathway signature could predict the prognosis of BRCA patients between these two subgroups independently in the TCGA dataset, which was confirmed in another two cohorts from the GEO dataset. In the TCGA dataset, 5-year OS rate was 78% (95% CI: 73-84) vs. 82% (95% CI: 77-87) for the PC and GC group (HR = 1.63 (95% CI: 1.17-2.28), p 0.004). The predictive power was similar in another two testing sets (HR > 1.20, p < 0.01). Finally, a nomogram is developed for clinical application, which integrated this signature and age to accurately predict the survival probability in BRCA patients. Conclusion. This 5-pathway signature correlated with immune derived lncRNAs was able to precisely predict the prognosis for patients with BRCA and provided a rich source characterizing immune-related lncRNAs and further informed strategies to target BRCA vulnerabilities.
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| GB/T 7714 | Liu, Bo , Zhu, Nian , Huo, Huixia et al. A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer [J]. | JOURNAL OF ONCOLOGY , 2022 , 2022 . |
| MLA | Liu, Bo et al. "A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer" . | JOURNAL OF ONCOLOGY 2022 (2022) . |
| APA | Liu, Bo , Zhu, Nian , Huo, Huixia , Long, Junqi , Ji, Xinchan , Li, Jinmeng et al. A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer . | JOURNAL OF ONCOLOGY , 2022 , 2022 . |
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Abstract ：
With the continuous development of bioinformatics, traditionally biological sequence analysis methods are insufficient to deal with the increasingly complex and huge biological data. In the face of this situation, deep learning has been gradually applied in biological analysis and made a series of progresses, which has become a hot research topic in biological data analysis with its advantages in processing high-dimensional data. The current research status was reviewed to better understand the new development of deep learning in the field of bioinformatics data analysis. First, the importance of applying deep learning were introduced. Second, representative deep learning models in the current application fields was described. Then, the application research status of deep learning in this field was analyzed. Finally, current limitations of deep learning in the bioinformatics field and the factors that should be considered in future development were illustrated in this paper. © 2022, Editorial Department of Journal of Beijing University of Technology. All right reserved.
Keyword ：
Bioinformatics; Biological sequences analysis; Deep learning; Gene; Nucleic acid; Protein Bioinformatics; Biological sequences analysis; Deep learning; Gene; Nucleic acid; Protein
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| GB/T 7714 | Zhang, J. , Wang, Z. , Liu, B. . Progress in the Applications of Deep Learning in Biological Sequences Analysis [深度学习在生物序列分析领域的应用进展] [J]. | Journal of Beijing University of Technology , 2022 , 48 (8) : 878-887 . |
| MLA | Zhang, J. et al. "Progress in the Applications of Deep Learning in Biological Sequences Analysis [深度学习在生物序列分析领域的应用进展]" . | Journal of Beijing University of Technology 48 . 8 (2022) : 878-887 . |
| APA | Zhang, J. , Wang, Z. , Liu, B. . Progress in the Applications of Deep Learning in Biological Sequences Analysis [深度学习在生物序列分析领域的应用进展] . | Journal of Beijing University of Technology , 2022 , 48 (8) , 878-887 . |
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Abstract ：
Upper limb kinematic analysis that has been employed in the clinical assessment of motion functions or rehabilitation training is traditionally tested manually with a goniometer. Nowadays, it is a trend to deploy different technology and devices including low-cost but accurate RGB cameras in order to save manual efforts. Among these, a new method using deep learning-based cameras has been investigated to provide the same ease and accessibility as a manual handheld goniometer. The key to measuring upper limb Range of Motion (ROM) using a camera is to estimate upper limb joints accurately. Many existing joint estimation algorithms focus on improving the accuracy performance but put the efficiency concerns aside. It is still challenging to apply those algorithms to low-capacity and budget-friendly devices, which is highly demanding in clinical scenarios. We propose a lightweight and fast deep learning model to estimate human pose and then use predicted joints to measure the range of motion for upper limb joints. Unlike other human pose estimation methods that learn and predict all major joints of the human body, the proposed model only focuses on the upper limb, which improves the accuracy and reduces the overhead of prediction. To further reduce model size and latency, our model is based on a compact neural network architecture, and parameters in the network are quantized to 8-bit precision. As a result, our model runs 4.1 times faster and is 15.5 times smaller compared with a full sized state of the art human pose estimation model. The proposed method is further evaluated on different upper limb functional tasks. Results show that our new method achieves a satisfying accuracy in ROM measurement and a high degree of agreement with a goniometer. Compared with the goniometer to measure ROM, our presented method is easier to operate and can be performed remotely, while still retaining good accuracy.
Keyword ：
2D body estimate 2D body estimate low-capacity device low-capacity device range of motion range of motion quantized convolutional neural network quantized convolutional neural network deep learning deep learning
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| GB/T 7714 | Yan, Xuke , Zhang, Linxi , Liu, Bo et al. A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment [J]. | 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA , 2022 : 53-60 . |
| MLA | Yan, Xuke et al. "A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment" . | 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA (2022) : 53-60 . |
| APA | Yan, Xuke , Zhang, Linxi , Liu, Bo , Qu, Guangzhi . A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment . | 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA , 2022 , 53-60 . |
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Abstract ：
针对服务计算、物联网、云计算、服务互联网、工业互联网、数字智慧服务等新兴产业对具有原始创新能力的软件设计人才需求，探讨如何围绕软件设计核心价值体系进行本科软件设计与体系结构课程内容建设及实践环节创新，提出以“知识型工程创新设计(KEID)”为核心价值的课程知识体系结构并介绍以开放式领域建模为基础的创新性实践环节设计。
Keyword ：
软件体系结构 软件体系结构 KIIC实践环节设计 KIIC实践环节设计 知识型工程创新设计(KEID) 知识型工程创新设计(KEID) 开放式领域建模 开放式领域建模
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| GB/T 7714 | 张建 , 刘博 , 朱青 et al. 软件设计与体系结构课程内容建设及创新探索 [J]. | 计算机教育 , 2022 , PageCount-页数: 5 (07) : 62-66 . |
| MLA | 张建 et al. "软件设计与体系结构课程内容建设及创新探索" . | 计算机教育 PageCount-页数: 5 . 07 (2022) : 62-66 . |
| APA | 张建 , 刘博 , 朱青 , 张丽 . 软件设计与体系结构课程内容建设及创新探索 . | 计算机教育 , 2022 , PageCount-页数: 5 (07) , 62-66 . |
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| GB/T 7714 | Liu, Bo , Ji, Xinchan , Li, Jinmeng et al. Integrative analysis identifies three molecular subsets in ovarian cancer [J]. | CLINICAL AND TRANSLATIONAL MEDICINE , 2022 , 12 (9) . |
| MLA | Liu, Bo et al. "Integrative analysis identifies three molecular subsets in ovarian cancer" . | CLINICAL AND TRANSLATIONAL MEDICINE 12 . 9 (2022) . |
| APA | Liu, Bo , Ji, Xinchan , Li, Jinmeng , Zhu, Nian , Long, Junqi , Zhuang, Xujie et al. Integrative analysis identifies three molecular subsets in ovarian cancer . | CLINICAL AND TRANSLATIONAL MEDICINE , 2022 , 12 (9) . |
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Abstract ：
本发明公开了基于节点级嵌入特征三维关系重建的图数据相似度方法，属于深度学习领域，首先通过孪生图卷积层和相似节点交互模块生成节点级嵌入特征三维关系，然后将节点级嵌入特征三维关系经过三维卷积提取特征，将三维特征经过Flatten层展开为一维，获得最终节点级关系向量。这一关系向量输入到由全连接层构成的结果输出模块得到预测输出。这一预测输出与实际的标签值进行比较，通过损失函数和反向传播算法对整体模型参数进行更新以达到学习的目的。完成训练的DeepSIM‑3D模型能高效可靠地计算输入的两个图结构数据的相似度。
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| GB/T 7714 | 刘博 , 武嘉慧 , 王志晗 et al. 基于节点级嵌入特征三维关系重建的图数据相似度方法 : CN202210059012.6[P]. | 2022-01-18 . |
| MLA | 刘博 et al. "基于节点级嵌入特征三维关系重建的图数据相似度方法" : CN202210059012.6. | 2022-01-18 . |
| APA | 刘博 , 武嘉慧 , 王志晗 , 张冀东 . 基于节点级嵌入特征三维关系重建的图数据相似度方法 : CN202210059012.6. | 2022-01-18 . |
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Abstract ：
本发明公开了一种基于特征交互神经张量网络的图结构数据相似度计算方法，即DeepSIM模型，属于深度学习领域，首先使用共享权值的图卷积层构建孪生图卷积模块用以获得两个图结构数据的一对节点级嵌入。然后将这一对节点级嵌入输入图注意力层以聚合节点级信息得到图级嵌入。针对图级嵌入，本发明提出的FINTN能够有效推理二者之间的关联并输出固定维度的关系向量。这一关系向量输入到由全连接层构成的结果输出模块得到预测输出。这一预测输出与实际的标签值进行比较，通过损失函数和反向传播算法对整体模型参数进行更新以达学习的目的。完成训练的DeepSIM模型能高效可靠地计算输入的两个图结构数据的相似度。
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| GB/T 7714 | 刘博 , 王志晗 , 张冀东 et al. 一种基于特征交互神经张量网络的图结构数据相似度计算方法 : CN202210052851.5[P]. | 2022-01-18 . |
| MLA | 刘博 et al. "一种基于特征交互神经张量网络的图结构数据相似度计算方法" : CN202210052851.5. | 2022-01-18 . |
| APA | 刘博 , 王志晗 , 张冀东 , 武嘉慧 . 一种基于特征交互神经张量网络的图结构数据相似度计算方法 : CN202210052851.5. | 2022-01-18 . |
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