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Review of cervical cell segmentation

Multimedia Tools and Applications 2024年 1-40页

作者： Huang, Qian Zhang, Wei Chen, Yulin Chen, Junzhou Yang, Zheng College of Computer Science and Software Engineering Hohai University Nanjing China Nanjing Huiying Electronic Technology Corporation Nanjing China

Cervical cell segmentation is a significant task in medical image analysis and can be used for screening various cervical diseases. In recent years, substantial progress has been made in cervical cell segmentation techniques, leading to notable improvements in the performance of cervical cancer auxiliary diagnostic systems. This review summarizes and analyzes the recent research on cervical cell segmentation. The main contents include an introduction to cervical cell segmentation datasets, commonly used evaluation metrics, and various segmentation methods. Currently, mainstream segmentation methods can be classified into two categories: traditional and deep learning-based. Building upon this foundation, we unfold according to the context of segmentation objectives, evaluating the performance of each method in achieving specific segmentation objectives and exploring the relationships among different methods. Through this review, other researchers can clearly understand the development of cervical cell segmentation technology and future trends, and explore new methods and technologies based on integrating and sorting out existing technologies, so as to help cervical cancer auxiliary diagnosis systems achieve more accurate cell image segmentation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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Leanor: A Learning-Based Accelerator for Efficient Approximate Nearest Neighbor Search via Reduced Memory Access 24

Leanor: A Learning-Based Accelerator for Efficient Approxima...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Yi Liu, Huan Yuan, Jianan Chen, Jiaxian Wang, Tianyu Ma, Chenlin Mao, Rui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400706011

Approximate Nearest Neighbor Search (ANNS) is a classical problem in data science. ANNS is both computationally-intensive and memory-intensive. As a typical implementation of ANNS, Inverted File with Product Quantization (IVFPQ) has the properties of high precision and rapid processing. However, the traversal of non-nearest neighbor vectors in IVFPQ leads to redundant memory accesses. This significantly impacts retrieval efficiency. A promising approach involves the utilization of learned indexes, leveraging insights from data distribution to optimize search efficiency. Existing learned indexes are primarily customized for low-dimensional data. How to tackle ANNS in high-dimensional vectors is a challenging *** paper introduces Leanor, a learned-index-based accelerator for the filtering of non-nearest neighbor vectors within the IVFPQ framework. Leanor minimizes redundant memory access, thereby enhancing retrieval efficiency. Leanor incorporates a dimension reduction component, mapping vectors to one-dimensional keys and organizing them in a specific order. Subsequently, the learned index leverages this ordered representation for rapid predictions. To enhance result accuracy, we conduct a thorough analysis of model errors and introduce a specialized index structure named Learned Index Forest (LIF). The experimental results show that, compared to representative approaches, Leanor can effectively filter out non-neighboring vectors within IVFPQ, leading to a substantial enhancement in retrieval efficiency. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Vectors

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SFTCS: Multiform Semantic Fusion Based on Transformer for Code Summarization 14

SFTCS: Multiform Semantic Fusion Based on Transformer for Co...

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14th International Conference on software Technology and engineering, ICSTE 2024

作者： Yang, Jun Ao, Longhao Qi, Rongzhi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350378955

Automatic code summarization aims to create co-herent natural language descriptions for code snippets. Recent studies indicate that integrating additional code representation structures improve the quality of generated summaries beyond token sequence-based approaches. Current technical research mainly concentrates on the graph structure information of code snippets. However, using a graph method makes it challenging to extract meaningful semantic information from the code snippets. To address the issue, we investigate how to learn more about code semantics and information from the perspective of code text feature representation. Consequently, we propose a novel model named SFTCS. SFTCS employs a conjoint feature fusion strategy, focusing on two types of information in code snippets: code token sequence features and FGT features based on Abstract Syntax Trees (AST) sequence and graph sequence. An N-adjacency ma-trix based on AST features is constructed, and the corresponding feature for AST block are optimized through a graph-based structure. Subsequently, a FGC decoder processes the two types of features. With the aid of an attention mechanism for feature enhancement, the SFTCS model ultimately predicts the results concretely. Evaluations on two publicly available Java datasets validate the effectiveness of SFTCS, showing its superiority over six advanced code summarization models. Ablation studies further elucidate the contributions of each component within the SFTCS model. © 2024 IEEE.

关键词： Semantics

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Study on the Rainfall-flood Association Pattern Database Based on Image Features 7

Study on the Rainfall-flood Association Pattern Database Bas...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Yu, Yufeng Cai, Difei Wu, Panqi Liu, Weifu Wang, Xuhang Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798350352009

Precipitation images can clearly reflect the rainfall spatio-temporal features and play an important role in hydrological analysis and flood forecasting. However, it is challenging to mine the association response relationship between rainfall and flood events. This paper proposed a rainfall-flood association pattern database construction method based on rainfall image features. First, the rainfall pattern database and flood pattern database are constructed respectively through the steps of hydrological process segmentation, feature extraction, similarity measurement and cluster analysis. Additionally, association rule mining algorithm is used to conduct rainfall-flood association pattern mining and consrtuct the rainfall-flood association pattern database. The experimental results in the Jialing River basin show that the method proposed in this paper can effectively mine the rainfall-flood association pattern database and provide technical support for intelligent flood prediction based on historical scene patterns. © 2024 IEEE.

关键词： Rain

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SRE-KGC : A Knowledge Graph Completion Model Based on Hidden Graph Structure and Relational Semantic Enhancement

SRE-KGC : A Knowledge Graph Completion Model Based on Hidden...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Sijun Xu, Guoyan Sun, Shuangyang College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9781665410205

Knowledge Graph Completion (KGC) endeavors to use existing knowledge graph data for predicting missing elements in triples. Recently, due to the efficiency of graph neural networks (GNNs) in capturing topological structures and the effectiveness of text descriptions in supplementing semantic information, numerous models integrating graph structures and entity descriptions have emerged. However, these approaches typically focus on aggregating neighboring feature information and overlook mining hidden structural information. Furthermore, they often append textual descriptions to entities independently without considering the semantics within specific relations. Hence, we propose the SRE-KGC model to address these challenges. First, while aggregating neighbors, we analyze and mine the hidden structure in the neighborhood from the perspective of entities and relations;then we introduced a dual-layer attention mechanism to extract the most pertinent textual information towards relations from both the relational semantic level and the neighbor semantic level respectively;finally, the two learned features are fused and sent to the decoder for scoring. Experiments demonstrate that our model delivers superior performance. © 2024 IEEE.

关键词： Knowledge graph

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Enhancing Gene Cluster Identification and Classification in Bacterial Genomes Through Synonym Replacement and Deep Learning

Enhancing Gene Cluster Identification and Classification in ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Du, Zhihua Zhong, Ningyu Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350386226

Natural products, abundant in biological activities, are synthesized by bacteria during secondary metabolism, yielding a plethora of antibiotics, anticancer drugs, and other potential therapeutics. These compounds are directed by biosynthetic gene clusters (BGCs), which encode the necessary enzymes and regulatory elements for their production. With the rapid advancement of sequencing technology, the number of fully sequenced bacterial genomes has exponentially increased, providing researchers with an unprecedented opportunity to discover more BGCs. However, current identification methods are significantly constrained by their reliance on database quantity and predominantly utilize local features inherent in the BGC sequences. In this study, we introduce Biosynthetic Gene Clusters Catcher: GCN-BERT(BGCCGB), a deep learning model leveraging natural language processing for the identification of BGCs in bacterial genomes and classification of BGCs product types. Our approach represents BGCs as sequences represented by protein family (Pfam) identifiers and performs data augmentation on existing BGC data by introducing a synonym replacement method. Local and global features in BGCs sequences are then integrated by BERT and its self-attention mechanism, in conjunction with Graph Convolutional Networks (GCN). The experiment results indicate that BGCCGB effectively learns the features of BGCs, localizes BGCs in bacterial genomes, and predicts the product types of BGCs. Notably, BGCCGB demonstrates a significant performance improvement over previous methods, with an average precision 6.9% in BGC identification. These enhancements underscore the potential of BGCCGB in advancing the discovery and understanding of natural products synthesized by bacteria. © 2024 IEEE.

关键词： Genome

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MFM: Multimodal Sentiment Analysis Based on Modal Focusing Model

MFM: Multimodal Sentiment Analysis Based on Modal Focusing M...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Shuangyang Xu, Guoyan Lu, Sijun College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9781665410205

Multimodal sentiment analysis integrates various modalities of information to collectively inform decision-making processes. Previous studies often treat different modal features equally or emphasize textual information as the primary consideration. However, when the modalities in the sample contain different sentiment information, these methods may not be able to effectively deal with this situation. To solve this problem, we propose a multimodal sentiment analysis model focusing on each modality (MFM). In this paper, we separately integrate each modality as a primary modality interacting with other secondary modal information so that each modality can play a leading role. In addition, we use shared mask in modal interaction to capture important information in the secondary modality related to the primary modality, and improve the effectiveness of the information interaction process. The model is evaluated against baseline models using the MOSI and MOSEI multimodal sentiment analysis datasets. The experimental results show that the model achieves better performance, thereby validating its effectiveness in multimodal sentiment analysis tasks. © 2024 IEEE.

关键词： Decision making

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Design and Implementation of an Agricultural Logistics Control System Based on IoT Techology

Design and Implementation of an Agricultural Logistics Contr...

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2024 International Conference on Advanced Control Systems and Automation Technologies, ACSAT 2024

作者： Sun, Pengyu Zhang, Zhiquan Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331541668

In today's society, the agricultural logistics and transportation industry faces numerous challenges in meeting the demands for fresh produce preservation and efficient transport. Insufficient environmental monitoring and information management during transportation lead to increased logistics costs and high produce spoilage rates. A high-quality, efficient logistics control system is essential for the agricultural supply chain. This paper presents a logistics control system based on IoT technology, centered on the ESP32 chip, utilizing ESP-NOW and MQTT protocols for communication, integrating multi-sensor data and RFID technology. A sensor module and host gateway were designed, along with a cloud platform visualization dashboard. Through this system, users can view sensor data, track item dynamics, and effectively monitor and control the agricultural logistics process in real-time, enhancing both the efficiency and quality of agricultural logistics. © 2024 IEEE.

关键词： Spoilage

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Leveraging computer Vision for Automatic Modulation Classification: Insights from Spectrum and Constellation Diagram Analysis 27th

Leveraging Computer Vision for Automatic Modulation Classifi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Wenjie Luo, Qiuming College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031783821

Automated modulation recognition is a challenging task in communication systems. Leveraging recent advancements in transfer learning, this paper proposes a novel method for automatic modulation recognition using transferred computer vision models. The method allows fine-tuning of the vision models to recognize modulation signals through spectrum and constellation diagrams. Experiments on the Radioml dataset demonstrate that the proposed method outperforms recent traditional methods by 8.97%, with an average accuracy of 0.5732. An ablation study confirms the effectiveness of using spectrum and constellation diagrams. This study verifies the feasibility of transferring vision models to AMC tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal modulation

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