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SSRN 2024年

作者： Chen, Xi Xu, Xiaoyu Tong, Lyuyang Zhao, Huangxuan Du, Bo School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan430072 China The Obstetrical Department Wuhan University People’s Hospital East Branch Wuhan430072 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430072 China

Early prenatal screening of fetal nasal bone (FNB) development is crucial for detecting chromosomal abnormalities. Existing deep learning approaches primarily focus on detection rather than diagnosis of FNB. This paper introduces an early prenatal FNB development automated diagnostic system (FNB-ADS), which employs a cascaded hierarchical filtering method to reduce noise interference in mid-sagittal plane ultrasound images. Specifically, the system employs YOLOv8 for precise FNB localization, segments the nasal bone, tip, and prenasal skin using a specially designed lightweight segmentation network, and diagnoses developmental abnormalities using Resnet34 classification methods. Furthermore, this paper has collected and publicly released the FNB-UDV dataset, which includes a detection subset and a video subset. The detection subset contains 1,007 two-dimensional ultrasound images, while the video subset comprises 12 ultrasound videos. Upon a comprehensive evaluation, the diagnostic accuracy of FNB-ADS reaches 92.37% with a processing time of 0.14 seconds per image, and the video diagnostic accuracy is 98.69% with a per-frame inference speed of 0.37 seconds in the FNB-UDV dataset. Representing the first deep-learning approach tailored specifically for early pregnancy FNB ultrasound video diagnosis, FNB-ADS significantly enhances the standardization of diagnostic procedures and reduces the dependence on subjective clinical assessments. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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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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MambaSAM: A Visual Mamba-Adapted SAM Framework for Medical Image Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liang, Pengchen Shi, Leijun Pu, Bin Wu, Renkai Chen, Jianguo Zhou, Lixin Xu, Lite Chen, Zhuangzhuang Chang, Qing Li, Yiwei The Department of Nuclear Medicine Shanghai Children’s Hospital School of Medicine Shanghai Jiao Tong University Shanghai200062 China The School of Microelectronics Shanghai University Shanghai201800 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong The School of Software Engineering Sun Yatsen University Guangdong Province Zhuhai519000 China The Department Shanghai Key Laboratory of Gastric Neoplasms Department of Surgery Shanghai Institute of Digestive Surgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai201800 China

The Segment Anything Model (SAM) has shown exceptional versatility in segmentation tasks across various natural image scenarios. However, its application to medical image segmentation poses significant challenges due to the intricate anatomical details and domain-specific characteristics inherent in medical images. To address these challenges, we propose a novel VMamba adapter framework that integrates a lightweight, trainable Visual Mamba (VMamba) branch with the pre-trained SAM ViT encoder. The VMamba adapter accurately captures multiscale contextual correlations, integrates global and local information, and reduces ambiguities arising from local features only. Specifically, we propose a novel cross-branch attention (CBA) mechanism to facilitate effective interaction between the SAM and VMamba branches. This mechanism enables the model to learn and adapt more efficiently to the nuances of medical images, extracting rich, complementary features that enhance its representational capacity. Beyond architectural enhancements, we streamline the segmentation workflow by eliminating the need for prompt-driven input mechanisms. This results in an autonomous prediction model that reduces manual input requirements and improves operational efficiency. In addition, our method introduces only minimal additional trainable parameters, offering an efficient solution for medical image segmentation. Extensive evaluations of two medical image datasets demonstrate that our VMamba adapter framework achieves state-of-the-art performance. Specifically, on the ACDC dataset with limited training data, our method achieves an average Dice coefficient improvement of 0.18 and reduces the Hausdorff distance by 20.38 mm compared to the AutoSAM. © 2013 IEEE. All rights reserved.

关键词： Image segmentation

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point Computing Power of GPUs

Towards Faster Fully Homomorphic Encryption Implementation w...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Guang Fan Fangyu Zheng Lipeng Wan Lili Gao Yuan Zhao Jiankuo Dong Yixuan Song Yuewu Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing China Ant Group Hangzhou China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Cyber Security University of Science and Technology of China Hefei China

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its large-scale application, highlighting the urgent requirements of high-performance FHE *** noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each road-map. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7× to 16.7×. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2× speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

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EC-GAN: Inferring brain effective connectivity via generative adversarial networks 34

EC-GAN: Inferring brain effective connectivity via generativ...

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34th AAAI Conference on Artificial Intelligence, AAAI 2020

作者： Liu, Jinduo Ji, Junzhong Xun, Guangxu Yao, Liuyi Huai, Mengdi Zhang, Aidong Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Computer Science and Engineering SUNY at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9781577358350

Inferring effective connectivity between different brain regions from functional magnetic resonance imaging (fMRI) data is an important advanced study in neuroinformatics in recent years. However, current methods have limited usage in effective connectivity studies due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for inferring effective connectivity based on generative adversarial networks (GAN), named as EC-GAN. The proposed framework EC-GAN infers effective connectivity via an adversarial process, in which we simultaneously train two models: a generator and a discriminator. The generator consists of a set of effective connectivity generators based on structural equation models which can generate the fMRI time series of each brain region via effective connectivity. Meanwhile, the discriminator is employed to distinguish between the joint distributions of the real and generated fMRI time series. Experimental results on simulated data show that EC-GAN can better infer effective connectivity compared to other state-of-the-art methods. The real-world experiments indicate that EC-GAN can provide a new and reliable perspective analyzing the effective connectivity of fMRI data. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Generative adversarial networks

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User-aware topic modeling of online reviews

User-aware topic modeling of online reviews

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作者： Pu, Xiaojia Wu, Gangshan Yuan, Chunfeng State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

The online reviews are one type of social media which are opinions generated by the users to comment on some special items. Since the sentiments are dependent on topics, probabilistic topic models have been widely used for sentiment analysis. However, most of existing methods only model the text, but rarely consider the users, who express the opinions, and the items, which the opinions are expressed on. Different users are usually concerned with different topics and use different sentiment expressions, a lenient user might tend to give positive review than a critical user. High-quality items tend to receive positive reviews than low-quality items. To better model the topics and sentiments, we argue that it is essential to explore reviews as well as users and items. To this end, we propose a novel model called User Item Sentiment Topic (UIST) which incorporates users and items for topic modeling and produces topic–word, user–topic, and item–topic distributions simultaneously. Extensive experiments on several datasets demonstrate the advantages and effectiveness of our method. The extracted topics with our method are more coherent and informative;consequently, the performance of sentiment classification is also improved. Furthermore, the user preference obtained with our method could be utilized for many personalized applications. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Sentiment analysis

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Low-Speed Injection Attack Detection on CAN Bus 3rd

Low-Speed Injection Attack Detection on CAN Bus

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3rd International Conference on Frontiers in Cyber Security, FCS 2020

作者： Wang, Chundong Li, Chuang An, Tongle Cheng, Xiaochun Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Department of Computer Science Middlesex University LondonNW4 4BT United Kingdom

ISBN: (纸本)9789811597381

The car CAN (Controller Area Network) bus message injection attacks seriously affects various functions of the safety of cars, life and property. However, low-speed injection attack is detection inconspicuous in a majority of existing researches. This paper proposes a self-contained low-speed injection attacks detection system including whole detection process and principle. This paper first analyzes the feasibility of low-speed injection attacks;then we propose to use LOF (Local Outlier Factor) to detect the injection attack, and compare with the previous detection algorithms. Experimental results show that our algorithm has obvious advantages in detection rate over the previous algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Exploring overall opinions for document level sentiment classification with structural SVM

Exploring overall opinions for document level sentiment clas...

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作者： Pu, Xiaojia Wu, Gangshan Yuan, Chunfeng State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

As a fundamental task of sentiment analysis, document level sentiment classification aims to predict user’s overall sentiment (e.g., positive or negative) towards the target in a document. The document usually consists of various opinion sentences towards different aspects with different sentiments. Therefore, the overall opinion towards the whole target should play a more important role in document sentiment prediction. However, most existing methods for the task treat all sentences of the document equally. Thus, they are easy to encounter difficulty when the sentiments of most aspect opinion sentences are not coherent with the overall sentiment. To address this, we propose a novel method for document sentiment classification which adequately explores the effect of overall opinion sentences. In our method, firstly, multiple features are exploited to recognize candidate overall opinion sentences, and then a structural SVM is utilized to encode the overall opinion sentences for document sentiment classification. Experiments on several public available datasets including product reviews and movie reviews show the effectiveness of our method. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Sentiment analysis

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Extended Abstract of Graph4Web: A Relation-Aware Graph Attention Network for Web Service Classification

Extended Abstract of Graph4Web: A Relation-Aware Graph Atten...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Kunsong Zhao Jin Liu Zhou Xu Xiao Liu Lei Xue Zhiwen Xie Yuxuan Zhou Xin Wang School of Computer Science Wuhan University Wuhan China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) China School of Big Data and Software Engineering Chongqing University Chongqing China School of Information Technology Deakin University Geelong Australia Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Engineering and Computer Science Syracuse University Syracuse USA

software reuse, as a means to develop new software products with similar functions by virtue of existing software components, has become a popular way during the software development process. In particular, as the service-oriented architecture became popular, web services turned into an indispensable part in modem software development Web services provide a basic composition with high cohesion and loose coupling to support responses among heterogeneous software components, which is the valuable resources for software reuse. The popular web service repositories, such as Programmable Web, contain a mass of web services for beginners and developers to choose from. Nevertheless, the large number of web services also makes it difficult to select the suitable services. Thus, the key to reuse software components lies in how to find appropriate web services from repositories to meet developers requirements in specific application scenarios.

关键词： Couplings Web services Modems Service-oriented architecture software reusability

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Cooperation Learning Enhanced Colonic Polyp Segmentation Based on Transformer-CNN Fusion

arXiv

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arXiv 2023年

作者： Wang, Yuanyuan Deng, Zhaohong Lou, Qiongdan Hu, Shudong Choi, Kup-Sze Wang, Shitong The School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Media Design and Software Technology Wuxi214122 China The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China and ZJLab Shanghai200433 China Department of Radiology Affiliated Hospital of Jiangnan University 1000 Hefeng Road Wuxi214122 China The School of the Centre for Smart Health The Hong Kong Polytechnic University Hong Kong

Traditional segmentation methods for colonic polyps are mainly designed based on low-level features. They could not accurately extract the location of small colonic polyps. Although the existing deep learning methods can improve the segmentation accuracy, their effects are still unsatisfied. To meet the above challenges, we propose a hybrid network called Fusion-Transformer-HardNetMSEG (i.e., Fu-TransHNet) in this study. Fu-TransHNet uses deep learning of different mechanisms to fuse each other and is enhanced with multi-view collaborative learning techniques. Firstly, the Fu-TransHNet utilizes the Transformer branch and the CNN branch to realize the global feature learning and local feature learning, respectively. Secondly, a fusion module is designed to integrate the features from two branches. The fusion module consists of two parts: 1) the Global-Local Feature Fusion (GLFF) part and 2) the Dense Fusion of Multi-scale features (DFM) part. The former is built to compensate the feature information mission from two branches at the same scale;the latter is constructed to enhance the feature representation. Thirdly, the above two branches and fusion modules utilize multi-view cooperative learning techniques to obtain their respective weights that denote their importance and then make a final decision comprehensively. Experimental results showed that the Fu-TransHNet network was superior to the existing methods on five widely used benchmark datasets. In particular, on the ETIS-LaribPolypDB dataset containing many small-target colonic polyps, the mDice obtained by Fu-TransHNet were 12.4% and 6.2% higher than the state-of-the-art methods HardNet-MSEG and TransFuse-s, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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