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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yijia Chen 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 Singapore Faculty of Engineering The University of Sydney Australia

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model’s accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively.

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wen Zhu 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 Singapore Faculty of Engineering The University of Sydney Australia

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything" to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA’s LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS’s unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes.

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Message from the InSTA 2022 chairs

Proceedings - 2022 IEEE 14th International Conference on Sof...

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Proceedings - 2022 IEEE 14th International Conference on software testing, Verification and Validation Workshops, ICSTW 2022 2022年 xvii页

作者： Masuda, Satoshi Nishi, Yasuharu Hagar, Jon Katayama, Tetsuro Liu, Qin Liu, Zhenyu Tsuda, Kazuhiko Yumoto, Tsuyoshi Takeda, T. Zhang, W. Hiruta, Y. Suhara, H. Masuda, S. Suzuki, K. Hagar, J. Tokyo City University Japan The University of Electro-Communications Japan Grand Software Testing United States University of Miyazaki Japan Tongji University China Shanghai Key Laboratory of Computer Software Testing and Evaluation China China University of Tsukuba Japan Japan Npo ASTER Japan Japan Software Bug Prediction Model Based on Mathematical Graph Features Metrics Japan Obtaining Fuzzing Results with Different Timeouts

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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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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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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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Instance and Category Supervision are Alternate Learners for Continual Learning

Instance and Category Supervision are Alternate Learners for...

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International Conference on computer Vision (ICCV)

作者： Xudong Tian Zhizhong Zhang Xin Tan Jun Liu Chengjie Wang Yanyun Qu Guannan Jiang Yuan Xie East China Normal University Shanghai Key Laboratory of Computer Software Testing & Evaluating Chongqing Institute of East China Normal University Tencent YouTu Lab Xiamen University Contemporary Amperex Technology Co. Limited

Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills. Yet, current CL algorithms tend to incur class-level forgetting as the label information is often quickly overwritten by new knowledge. This motivates attempts to mine instance-level discrimination by resorting to recent self-supervised learning (SSL) techniques. However, previous works have pointed out that the self-supervised learning objective is essentially a trade-off between invariance to distortion and preserving sample information, which seriously hinders the unleashing of instance-level *** this work, we reformulate SSL from the information-theoretic perspective by disentangling the goal of instance-level discrimination, and tackle the trade-off to promote compact representations with maximally preserved invariance to distortion. On this basis, we develop a novel alternate learning paradigm to enjoy the complementary merits of instance-level and category-level supervision, which yields improved robustness against forgetting and better adaptation to each task. To verify the proposed method, we conduct extensive experiments on four different benchmarks using both class-incremental and task-incremental settings, where the leap in performance and thorough ablation studies demonstrate the efficacy and efficiency of our modeling strategy.

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Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification

arXiv

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arXiv 2024年

作者： Shi, Jiangming Yin, Xiangbo Chen, Yeyun Zhang, Yachao Zhang, Zhizhong Xie, Yuan Qu, Yanyun Institute of Artificial Intelligence Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a promising yet highly challenging retrieval task. The key challenges in USL-VI-ReID are to accurately generate pseudo-labels and establish pseudo-label correspondences across modalities without relying on any prior annotations. Recently, clustered pseudo-label methods have gained more attention in USL-VI-ReID. However, most existing methods don’t fully exploit the intra-class nuances, as they simply utilize a single memory that represents an identity to establish cross-modality correspondences, resulting in noisy cross-modality correspondences. To address the problem, we propose a Multi-Memory Matching (MMM) framework for USL-VI-ReID. We first design a simple yet effective Cross-Modality Clustering (CMC) module to generate the pseudo-labels through clustering together both two modality samples. To associate cross-modality clustered pseudo-labels, we design a Multi-Memory Learning and Matching (MMLM) module, ensuring that optimization explicitly focuses on the nuances of individual perspectives and establishes reliable cross-modality correspondences. Finally, we design a Soft Cluster-level Alignment (SCA) loss to narrow the modality gap while mitigating the effect of noisy pseudo-labels through a soft many-to-many alignment strategy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the reliability of the established cross-modality correspondences and the effectiveness of MMM. © 2024, CC BY.

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

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

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Smart World Congress (SWC), IEEE

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

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.

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Test Case Generation for Web Application Based on Markov Reward Process

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Journal of Physics: Conference Series 2021年第1期1792卷

作者： Jing Cao Xiaoqiang Liu Hui Guo Lizhi Cai Yun Hu College of Computer Science and Technology Donghua University Shanghai 201620 China Shanghai Key Laboratory of Computer Software Testing &Evaluation Shanghai 201112 China

Web applications often face continuous updating due to functional change or UI renew, while it remains a challenge to guarantee their correctness. The goal of software testing is to find defects in a limited time range whereas exhaustive testing is an ideal yet time-consuming process. In this research, we propose an approach to generating test cases automatically based on the Markov reward process which innovatively contains a reward function for test results to guide the generation of test cases. By using the N-step algorithm, this approach can generate the test flow with the highest risk priority which can capture software defects as quickly as possible. The experiment on an e-commerce system shows that there is significant improvement on the defect detection capability of test cases generated through Markov reward process.

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