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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： QiSong Wang JiaRou Wu YingZhuo Wang HuiLi Qu Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Australia

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.

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CHESS: Learning User Correlation from Semantic-related Cascades for Diffusion Prediction 9

CHESS: Learning User Correlation from Semantic-related Casca...

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9th IEEE Smart World Congress, SWC 2023

作者： Li, Huacheng Xia, Chunhe Wang, Tianbo Zhao, Haopeng Beihang University Key Laboratory of Beijing Network Technology Beijing China Guangxi Collab. Innov. Ctr. of Multi-Src. Info. Intgn. and Intelligent Proc. Guangxi Norm. Univ. Guilin China Beihang University School of Cyber Science and Technology Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9798350319804

Understanding the information diffusion trajectory in online social networks has become an ever-growing prominent research direction in the information era. Users interact with social information they are interested in, and cascades record diffusion trajectories of massive social information. It implies two characteristics of diffusion cascades: topic sharing among different cascades and infected user correlation within the same cascades. However, previous works ignore these two characteristics and primarily focus on whether each infected user is correctly predicted individually, which leads to learning biased diffusion models under the data-driven mode. To address this problem, we propose a novel architecture to exploit such latent user associations among topic-related cascades. First, we leverage the hypergraph and heterogeneous graph to exploit infected user correlations jointly and generate user feature vectors. Second, we devise an enhanced cascade similarity loss function to evaluate prediction results from delay (horizontal) and rank (vertical) scopes. Furthermore, we introduce the extra predictor to alleviate the interference with the primary model. Experiments on four public datasets validate the effectiveness of our proposed method and significantly outperform existing models on hits@κ and map@κ indicators. © 2023 IEEE.

关键词： heterogeneous hyper graph information diffusion prediction loss function social network analysis

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yawen Liu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Faculty of Engineering The University of Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature’s contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG’s predictive prowess, with additional support from other omics features.

关键词： Accuracy Biological system modeling Cause effect analysis Machine learning Predictive models Benchmark testing Complexity theory Bioinformatics Cancer

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chenhang Wang Yujian Jin Jiayi Liang YuHan Yang Shanling Nie Yang Hai Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Sydney Australia

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer.

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Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification

arXiv

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arXiv 2024年

作者： Shi, Jiangming Yin, Xiangbo Zhang, Yachao Zhang, Zhizhong Xie, Yuan Qu, Yanyun Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China Shanghai Innovation Institute China East China Normal University China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified people in infrared images to visible images without annotations, and vice versa. USVI-ReID is a challenging yet under-explored task. Most existing methods address the USVI-ReID using cluster-based contrastive learning, which simply employs the cluster center as a representation of a person. However, the cluster center primarily focuses on commonality, overlooking divergence and variety. To address the problem, we propose a Progressive Contrastive Learning with Hard and Dynamic Prototypes method for USVI-ReID. In brief, we generate the hard prototype by selecting the sample with the maximum distance from the cluster center. We theoretically show that the hard prototype is used in the contrastive loss to emphasize divergence. Additionally, instead of rigidly aligning query images to a specific prototype, we generate the dynamic prototype by randomly picking samples within a cluster. The dynamic prototype is used to encourage the variety. Finally, we introduce a progressive learning strategy to gradually shift the model’s attention towards divergence and variety, avoiding cluster deterioration. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method. © 2024, CC BY.

关键词： Contrastive Learning

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Survey on testing of Autonomous Driving Systems 3

Survey on Testing of Autonomous Driving Systems

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3rd International Workshop on Experience with SQuaRE Series and Its Future Direction, IWESQ 2021

作者： Chen, Wenjie Ma, Zeyu Wang, Chao Chen, Mingang Cai, Lizhi Software Engineering Institute Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

Before autonomous driving vehicles are commercialized, they need to undergo a series of rigorous tests. This paper first proposes the general process of autonomous driving system testing, and then summarizes the research progress of autonomous driving vehicle testing from the model, simulation, network, and vehicle levels, and analyzes the characteristics of various testing technologies. Finally, this paper gives suggestions on the development of autonomous driving testing technology. © 2021 for this paper by its authors

关键词： Autonomous vehicles

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Research on Quality Model and Measurement for Microservices 3

Research on Quality Model and Measurement for Microservices

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3rd International Workshop on Experience with SQuaRE Series and Its Future Direction, IWESQ 2021

作者： Yu, Jinchuan Ge, Jianxin Sun, Jixin Software Engineering Institute Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

In recent years, microservice architecture (MSA) has become popular. Emerging from the agile community, MSA implies a number of small, independently deployable microservices. They are characterized by low coupling, high cohesion, low complexity, and are more flexible and convenient in application, saving resource ***, there is limited research on quality models for MSA. Although MSA is a special form of service-oriented architecture, there are still some differences between the two that are hard to ignore, such as decentralization, smaller service size and encouraging technical heterogeneity. Therefore, it is difficult to directly apply the traditional quality model research of service-oriented architecture to MSA. Based on the above considerations, this paper proposes a quality model for MSA, which reflects the quality characteristics of microservices through 16 measures for each of the Functional Suitability, Flexibility, Interactivity, Performance Efficiency, Reliability and Security. At the same time, to address the shortage of theoretical validation of current research, this paper uses static analysis and dynamic analysis to validate the proposed measures and prove the rationality of its theory. © 2021 for this paper by its authors

关键词： Static analysis

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Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic Consistency

Multi-Signal Fusion of Social Diffusion Graph with Bi-Direct...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huacheng Li Chunhe Xia Tianbo Wang Wanshuang Lin Changnan Jiang Chen Chen Yuan Zhao Key Laboratory of Beijing Network Technology Beihang University Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University School of Cyber Science and Technology Beihang University Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

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Attribute-Based Non-Interactive Privacy-Protected Crowd-Sourcing software testing Engineer Selection Mechanism

Attribute-Based Non-Interactive Privacy-Protected Crowd-Sour...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Qiliang Yang Lizhi Cai Jiayu Gong Minlu Tang Yi Lin Shanghai Development Center of Computer Software Technology Cryptographic Standard Study & Security Evaluation Laboratory Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

The central management platforms in traditional crowd-sourced software testing networks are easy to become the target of attack by the enemies, and when the central management platform is attacked, it is easy to cause a single point of failure and bring the whole system down. In addition, when selecting software testing engineers for crowd-sourcing software testing tasks, most mechanisms select engineers based on their credits. This approach is too one-sided and fails to achieve fine-grained engineer selection. In order to solve above problems, we have proposed a non-interactive and fine-grained crowd-sourcing software testing engineer selection mechanism. In our mechanism, the task publisher can select engineers according to their attributes. Next, we have given an implementation of our non-interactive software testing engineer selection mechanism by applying QAP. The privacy of task publisher and engineers is protected. The security proof is also given. Simulation results show that our mechanism is feasible.

关键词： software testing Privacy Simulation software quality Reliability engineering software reliability Security

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A universal multiple instance learning framework for whole slide image analysis

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computers in Biology and Medicine 2024年 178卷 108714-108714页

作者： Zhang, Xueqin Liu, Chang Zhu, Huitong Wang, Tianqi Du, Zunguo Ding, Weihong College of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Department of Pathology Huashan Hospital Affiliated to Fudan University Shanghai200040 China Department of Urology Huashan Hospital Affiliated to Fudan University Shanghai200040 China

Background: The emergence of digital whole slide image (WSI) has driven the development of computational pathology. However, obtaining patch-level annotations is challenging and time-consuming due to the high resolution of WSI, which limits the applicability of fully supervised methods. We aim to address the challenges related to patch-level annotations. Methods: We propose a universal framework for weakly supervised WSI analysis based on Multiple Instance Learning (MIL). To achieve effective aggregation of instance features, we design a feature aggregation module from multiple dimensions by considering feature distribution, instances correlation and instance-level evaluation. First, we implement instance-level standardization layer and deep projection unit to improve the separation of instances in the feature space. Then, a self-attention mechanism is employed to explore dependencies between instances. Additionally, an instance-level pseudo-label evaluation method is introduced to enhance the available information during the weak supervision process. Finally, a bag-level classifier is used to obtain preliminary WSI classification results. To achieve even more accurate WSI label predictions, we have designed a key instance selection module that strengthens the learning of local features for instances. Combining the results from both modules leads to an improvement in WSI prediction accuracy. Results: Experiments conducted on Camelyon16, TCGA-NSCLC, SICAPv2, PANDA and classical MIL benchmark datasets demonstrate that our proposed method achieves a competitive performance compared to some recent methods, with maximum improvement of 14.6 % in terms of classification accuracy. Conclusion: Our method can improve the classification accuracy of whole slide images in a weakly supervised way, and more accurately detect lesion areas. © 2024 Elsevier Ltd

关键词： Image classification

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