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SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer

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computers in Biology and Medicine 2024年 173卷 108293-108293页

作者： Ma, Yulan Guo, Yuzhu Cui, Weigang Liu, Jingyu Li, Yang Wang, Yingsen Qiang, Yan Department of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Engineering Medicine Beihang University Beijing100191 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Software North University of China Taiyuan China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder–decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status. © 2024

关键词： Aggregates

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Position: Topological Deep Learning is the New Frontier for Relational Learning 41

Position: Topological Deep Learning is the New Frontier for ...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Birdal, Tolga Bronstein, Michael Carlsson, Gunnar Curry, Justin Gao, Yue Hajij, Mustafa Kwitt, Roland Liò, Pietro Di Lorenzo, Paolo Maroulas, Vasileios Miolane, Nina Nasrin, Farzana Ramamurthy, Karthikeyan Natesan Rieck, Bastian Scardapane, Simone Schaub, Michael T. Veličković, Petar Wang, Bei Wang, Yusu Wei, Guo-Wei Zamzmi, Ghada Department of Mathematics The University of Manchester Manchester United Kingdom Department of Computing Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Mathematics Stanford University Stanford United States BlueLightAI Inc. United States University at Albany New York United States School of Software Tsinghua University Beijing China University of San Francisco San Francisco United States Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville United States Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara United States Department of Mathematics University of Hawai'i at Mānoa HI United States IBM Corporation New York United States Helmholtz Munich Munich Germany Technical University of Munich Munich Germany RWTH Aachen University Aachen Germany Google Deep-Mind United Kingdom School of Computing University of Utah Utah United States Computer Science and Engineering Department University of California San Diego San Diego United States Department of Mathematics Michigan State University East LansingMI United States University of South Florida Florida United States

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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CompanyName2Vec: Company Entity Matching Based on Job Ads

arXiv

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arXiv 2022年

作者： Ziv, Ran Gronau, Ilan Fire, Michael Efi Arazi School of Computer Science Reichman University Department of Software and Information Systems Engineering Ben-Gurion University

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and integration processes require completion before any data analytics, or further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights, supports data visualization, and guides further analytic approaches to the data. Here, we focus on organization entity matching. We introduce CompanyName2Vec, a novel algorithm to solve company entity matching (CEM) using a neural network model to learn company name semantics from a job ad corpus, without relying on any information on the matched company besides its name. Based on a real-world data, we show that CompanyName2Vec outperforms other evaluated methods and solves the CEM challenge with an average success rate of 89.3%. Copyright © 2022, The Authors. All rights reserved.

关键词： Data Analytics

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Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

arXiv

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arXiv 2023年

作者： Chen, Longfei Liu, Qianyu Zhang, Chenyang Huang, Yangkun Peng, Zhenhui Zeng, Haipeng Sun, Zhida Ma, Xiaojuan Li, Quan The School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging China The Department of Computer Science University of Illinois Urbana-Champaign United States Tandon School of Engineering New York University United States The School of Artificial Intelligence Sun Yat-Sen University China The School of Intelligent Systems Engineering Sun Yat-Sen University China The College of Computer Science and Software Engineering Shenzhen University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations. © 2023, CC BY.

关键词： Music

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Explainable Artificial Intelligence computer-Aided Diagnosis for Heart Myocardial Infarction

Explainable Artificial Intelligence Computer-Aided Diagnosis...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Radhwan A. A. Saleh Humam AbuAlkebash Alaa Aldeen Mohammed Daniel Addo Ertan Bütün Mugahed A. Al-Antari Mechatronics Engineering Department Kocaeli University Kocaeli Turkey School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Engineering Firat University Elazığ Turkey Department of Artificial Intelligence Sejong University Seoul Republic of Korea

Myocardial infarction (MI) is a significant cardiovascular condition known for its detrimental impact on the heart. The rapid and accurate diagnosis of MI is crucial for reducing mortality rates. In the realm of medical imaging interpretation, explainable artificial intelligence (XAI) has emerged as a promising research field. XAI offers a valuable tool to create interpretable saliency maps for each MRI slice, providing insights into the decision-making processes employed by AI models. Our proposed framework begins with the enhancement of MI images using Contrast Limited Adaptive Histogram Equalization. We integrate and adopt five deep learning models: InceptionV3, InceptionResNetV2, ResNet50V2, VGG16, and Xception, along with the Grad-CAM technique, to demonstrate the robustness of these models in MI detection. Among these models, InceptionResNetV2 achieved the highest accuracy with a score of 83.33%, and an impressive F-score of 88.10%. However, based on the Grad-CAM results, the most trustable and robust model is ResNet50V2, which consistently demonstrated its focus on regions of interest in both normal and abnormal predictions. These compelling findings should motivate researchers and stakeholders in the medical industry to consider implementing this framework in practice, ultimately contributing to the early and precise diagnosis of MI and reducing MI-related mortality rates.

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Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets

arXiv

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arXiv 2022年

作者： Kim, Jiwan Kim, Minchang Shin, Yeong-Gil Chung, Minyoung Department of Computer Science and Engineering Seoul National University Korea Republic of School of Software Soongsil University Korea Republic of

—Accurate three-dimensional perception is a fundamental task in several computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted as single-view depth-sensing devices owing to their efficient depth-sensing abilities. However, the depth quality of most RGB-D sensors remains insufficient owing to the inherent noise from a single-view environment. Recently, several studies have focused on the single-view depth enhancement of RGBD cameras. Recent research has proposed deep-learning-based approaches that typically train networks using high-quality supervised depth datasets. The performance of current learning-based depth enhancement techniques is highly dependent on the quality of the ground-truth (GT) depth dataset;however, such high-quality GT datasets are difficult to obtain. In this study, we developed a novel method for high-quality GT depth generation based on an RGB-D stream dataset. First, we defined consecutive depth frames in a local spatial region as a local frame set. Then, the depth frames were aligned to a certain frame in the local frame set using an unsupervised point cloud registration scheme. The registration parameters were trained based on an overfit-training scheme, which was primarily used to construct a single GT depth image for each frame set. The final GT depth dataset was constructed using several local frame sets, and each local frame set was trained independently. The primary advantage of this study is that a high-quality GT depth dataset can be constructed under various scanning environments using only the RGB-D stream dataset, which can be used as a single-view depth supervision dataset. Moreover, our proposed method can be used as a new benchmark GT dataset for accurate performance evaluations. We evaluated our GT dataset on previously benchmarked GT depth datasets and demonstrated that our method is superior to state-of-the-art depth enhancement frameworks. Copyright © 2022, The Authors. All righ

关键词： Image enhancement

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Mobile Medical Assistant System for Laboratory Report Analysis and Medical Drug Identification

Mobile Medical Assistant System for Laboratory Report Analys...

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Advancements in Computing (ICAC), International Conference on

作者： Deshani Warnakulasuriya Tharushi Dewangi Navodya Sewwandi Minoli Rathnayake Nuwan Kodagoda Kushanra Suriyawansha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month’s results of the medical lab report of the patient using machine learning.

关键词： Drugs Hospitals Image processing Organizations Machine learning Packaging Market research

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

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Application of Explicit Group Sparse Projection in Optimizing 5G Network Resource Allocation and Topology Design

Application of Explicit Group Sparse Projection in Optimizin...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Kodanda Rami Reddy Manukonda Hemanth Swamy R. Sankar Chidambaranathan C. M. Haribabu.k Saranya. Department of Consulting IBM Senior Software Engineer Motorola Solutions Department of EEE Chennai Institute of Technology Chennai Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE MLR Institute of Technology Hyderabad Department of AI & DS Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

The growing adoption of 5G networks in diverse domains like IoT, smart cities, healthcare, and industrial automation necessitates advanced optimization techniques. While existing approaches have shown promise, they are often limited by computational intensity, lack of adaptability, and specific focus on geometric constraints. This paper presents EGSP-5G, a novel methodology employing Explicit Group Sparse Projection (EGSP) for 5G network resource allocation and topology design. Its capability to achieve targeted sparsity levels allows for efficient utilization of network resources, thereby making it a versatile and powerful tool for various optimization tasks. Remarkably, EGSP-5G achieved a resource utilization rate of 90.02% and a throughput of 110.32 in networks comprising 1000 nodes. It stands as a robust and versatile solution for optimizing 5G networks across diverse application domains.

关键词： Technological innovation Automation 5G mobile communication Network topology Smart cities Medical services Throughput Topology Resource management Optimization

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Fuzzy Clustering and Autoencoder-Based Cybersecurity for EVs in a Blockchain-Enabled Smart Cloud Environment

Fuzzy Clustering and Autoencoder-Based Cybersecurity for EVs...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Subha B Sivakumar Nagarajan A. Thangam V. K. Somasekhar Srinivas RVS Praveen Kingshuk Das Bakshi Department of Professional Management Studies Kristu Jayanti College Kothanur India I & I Software Bethlehem USA Department of Mathematics Pondicherry University Community College Pondicherry India Department of Mathematics Mohan Babu University Tirupati India Department of Digital Engineering and Assurance Skyview Hyderabad India Department of Computer Science and Engineering Ideal Institute of Engineering Nadia India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rise of electric vehicles (EVs) has revealed several cybersecurity weaknesses, especially in data transmission inside cloud-based systems. Current cybersecurity algorithms for anomaly detection frequently struggle to manage the intricacy and magnitude of electric vehicle network data. This study presents an innovative cybersecurity system that integrates fuzzy clustering for soft anomaly detection, autoencoders for deep learning-driven feature extraction, and blockchain for secure data validation. The suggested model effectively tackles critical issues in anomaly detection, attaining an accuracy rate of 97.2%, an F1 score of 0.96, and a recall of 0.95, therefore surpassing current methodologies substantially. The results underscore the model's potential to identify and address cyber risks with enhanced precision and recall, providing a scalable and resilient solution for EV networks. Future improvements may concentrate on minimising computation time and augmenting detection efficacy in real-time systems, establishing this method as a viable option for bolstering EV cybersecurity in blockchain-integrated smart settings.

关键词： Wireless communication Accuracy Computational modeling Autoencoders Electric vehicles Feature extraction Real-time systems Blockchains Reliability Anomaly detection

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