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MSMMIL: Multi-scan Mamba-based Multiple Instance Learning for whole slide image classification

Knowledge-Based Systems 2025年 324卷

作者： Haiqin Zhong Meidan Ding Cheng Zhao Yongtao Zhang Tianfu Wang Baiying Lei National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Marshall Laboratory of Biomedical Engineering School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen 518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

Multiple instance learning has emerged as the primary technique in computational pathology for analyzing whole slide images (WSI). It typically emphasizes the most distinguishing instances using an attention mechanism. Despite significant progress, WSI classification still faces challenges, particularly in efficiently extracting discriminative features from long instance sequences that often contain a large amount of redundant or irrelevant information. To address this issue, this study introduces a new approach, named Multi-scan Mamba-based MIL (MSMMIL). The proposed framework introduces a Multi-scan Mamba module, which includes two novel complementary scanning strategies, grid scan and layer scan, an existing original scanning strategy, and three global context attention (GCA) blocks. The three scanning strategies to jointly model directional dependencies, restore structural continuity, and preserve spatial consistency, enabling more effective extraction of discriminative features from long instance sequences. While the lightweight GCA block is devised to learn the feature associations between different instances and amplify the key instances. Experimental results on two publicly available datasets, TCGA-Lung and Camelyon16, show that MSMMIL achieves state-of-the-art performance by ACC and AUC, reaching 89.81%, 95.44% and 92.87%, 96.41%, respectively. The code will be made publicly available on https://***/cool-breeze-and-rain/MSMMIL .

关键词： Classification Computational pathology Mamba Multiple instance learning Whole slide image

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Corrigendum to “An analysis of urban sprawl growth and prediction using remote sensing and machine learning techniques” [J. S. Am. Earth Sci. Volume 142 , 15 August 2024, 104988]

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Journal of South American Earth sciences 2025年 152卷

作者： Alanoud Al Mazroa Mashael Maashi Fadoua Kouki Kamal M. Othman Nahla Salih Mohamed Ahmed Elfaki Sabarunisha Begum S Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh 11543 Saudi Arabia Department of Financial and Banking Sciences Applied College at Muhail Aseer King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Biotechnology P.S.R. Engineering College Sivakasi 626140 India

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Deep Domain Adaptation for Pavement Crack Detection

arXiv

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arXiv 2021年

作者： Liu, Huijun Yang, Chunhua Li, Ao Huang, Sheng Feng, Xin Ruan, Zhimin Ge, Yongxin College of Computer Science Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Chongqing City Management College Chongqing401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China College of Computer Science And Engineering Chongqing University of Technology Chongqing400054 China Co. Ltd Chongqing400060 China

Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease Dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain. Copyright © 2021, The Authors. All rights reserved.

关键词： Crack detection

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Boosting Component-based Synthesis with API Usage Knowledge

Boosting Component-based Synthesis with API Usage Knowledge

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IEEE/ACM International Conference on Automated software engineering - Workshops (ASE Workshops)

作者： Jiaxin Liu Wei Dong Binbin Liu College of Computer Science National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781450381284

ISBN: (纸本)9781728172958

Component-based synthesis is one of the hottest research areas in automated software engineering. It aims to generate programs from a collection of components like Java library. However, the program space constituted by all the components in the library is fairly large, which leads to a vast number of candidate programs generated for a long time. The intractability of the program space affects the synthesis efficiency of the program and the size of the program generated. In this paper, we propose ITAs, a framework of iterative program synthesis via API usage knowledge from the Internet, which can significantly improve the efficiency of program synthesis. ITAs aims to constrain the program space by combining two main ideas. First, narrow down the program space from the outside via the guidance of API usage knowledge. Second, expand the program space from the inside via iterative strategy based on knowledge. For evaluation, we collect a set of programming tasks and compare our approach with a program synthesis tool on synthesizing these tasks. The experiment results show that Itas can significantly improve the efficiency of program synthesis, which can reduce the synthesis time by 97.1 % than the original synthesizer.

关键词： Task analysis Libraries Internet Programming Tools Natural languages Java

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Collision avoidance situation matching with vessel maneuvering actions identification from vessel trajectories

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International Journal of Performability engineering 2019年第6期15卷 1499-1507页

作者： Chen, Peng Shi, Guoyou Liu, Shuang Gao, Miao Navigation College Dalian Maritime University Dalian116026 China Department of Software Engineering Dalian Neusoft University of Information Dalian116030 China School of Computer Science and Engineering Dalian Minzu University Dalian116605 China

Vessel trajectories implied in AIS data are crucial to obtain a good understanding of the maritime traffic situation for shipping safety. Starting from raw AIS data, a trajectory database is created for vessels within surveillance area after parsing, noise reduction, and DBSCAN clustering. With mmsi as the key index, the trajectory for each vessel is extracted ordering by timestamp. To remove the time interval difference between points in trajectories, interpolation and cleaning are carried out on each vessel trajectory to get trajectories with equal time intervals. Through implied motion pattern computation between adjacent points in each trajectory, maneuvering actions can be identified. Then, sailing segments with continuous same maneuvering actions are merged. With sailing segments partition results, critical points are extracted for already known different collision avoidance situations. Trajectory similarity computation for different vessels are computed with our new multi-scale and multi-resolution trajectory matching method. Experiments for the recognition of collision avoidance situations show that the adoption of the matching algorithm with multi-scale and multi-resolution trajectories for different vessel pairs to complete collision avoidance situations analysis is effective and achieves good performance. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Collision avoidance

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Matching algorithms: Fundamentals, applications and challenges

arXiv

引用

arXiv 2021年

作者： Ren, Jing Xia, Feng Chen, Xiangtai Liu, Jiaying Hou, Mingliang Shehzad, Ahsan Sultanova, Nargiz Kong, Xiangjie The School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia The School of Software Dalian University of Technology Dalian116620 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people’s daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory’s basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

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Author name disambiguation in bibliographic databases: A survey

arXiv

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arXiv 2020年

作者： Shoaib, Muhammad Daud, Ali Amjad, Tehmina Department of Computer Science Comsats University Sahiwal Campus Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan

Entity resolution is a challenging and hot research area in the field of Information Systems since last decade. Author Name Disambiguation (AND) in Bibliographic Databases (BD) like DBLP1, Citeseer2, and Scopus3 is a specialized field of entity resolution. Given many citations of underlying authors, the AND task is to find which citations belong to the same author. In this survey, we start with three basic AND problems, followed by need for solution and challenges. A generic, five-step framework is provided for handling AND issues. These steps are;(1) Preparation of dataset (2) Selection of publication attributes (3) Selection of similarity metrics (4) Selection of models and (5) Clustering Performance evaluation. Categorization and elaboration of similarity metrics and methods are also provided. Finally, future directions and recommendations are given for this dynamic area of research. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Multi-Task Learning for Jointly Detecting Depression and Emotion

Multi-Task Learning for Jointly Detecting Depression and Emo...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yazhou Zhang Xiang Li Lu Rong Prayag Tiwari Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Shandong Computer Science Center (National Supercomputing Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Aalto University Helsinki Finland

ISBN: (纸本)9781665429825

Depression is a typical mood disease that makes people a persistent feeling of sadness and loss of interest and pleasure. Emotion thus comes into sight and is tightly entangled with depression in that one helps the understanding of the other. Depression and emotion detection has been a new research task. The central challenges in this task are multi-modal interaction and multi-task correlation. The existing approaches treat them as two separate tasks, and fail to model the relationships between them. In this paper, we propose an attentive multi-modal multitask learning framework, called AMM, to generically address such issues. The core modules are two attention mechanisms, viz. inter-modal $(I_{\mathrm{e}})$ and inter-task $(I_{t})$ attentions. The main motivation of $I_{\mathrm{e}}$ attention is to learn multi-modal fused representation. In contrast, I t attention is proposed to learn the relationship between depression detection and emotion recognition. Extensive experiments are conducted on two large scale datasets, i.e., DAIC and multi-modal Getty Image depression (MGID). The results show the effectiveness of the proposed AMM framework, and also shows that AMM obtains better performance for the main task, i.e., depression detection with the help of the secondary emotion recognition task.

关键词： Emotion recognition Correlation Mood Conferences Depression Multitasking Task analysis

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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A Mobile Cloud-Based eHealth Scheme

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computers, Materials & Continua 2020年第4期63卷 31-39页

作者： Yihe Liu Aaqif Afzaal Abbasi Atefeh Aghaei Almas Abbasi Amir Mosavi Shahaboddin Shamshirband Mohammed A.A.Al-qaness Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AINeijiang Normal UniversityNeijiang641100China Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Communication Faculty of Social SciencesUniversity of TehranTehran***Iran Department of Computer Science International Islamic UniversityIslamabad46000Pakistan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Kalman Kando Faculty of Electrical Engineering Obuda UniversityBudapest1034Hungary School of the Built Environment Oxford Brookes UniversityOxfordOX30BPUK Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam School of Computer Science Wuhan UniversityWuhan430072China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid ***,the field of health informatics is also considered as an extremely important *** work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing *** developed system has two front ends,the first dedicated for the user to perform the photographing of the trace *** the photographing is complete,mobile computing is used to extract the *** the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the *** this is done,the second interface,intended for the use of the physician,can download and view the trace from the *** data is securely held using a password-based authentication *** system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.

关键词： Cloud computing electrocardiograms health-care signal analysis signal processing

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