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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： artificial intelligence breast cancer digital pathology whole slide images

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IPGRN: An Integrated Progressive Gated Refinement Network for Breast Tumor Analysis

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IEEE Transactions on Consumer Electronics 2025年

作者： Dai, Zihao Huang, Guoheng Li, Yan He, Jianbin Yuan, Xiaochen Pun, Chi-Man Zhong, Guo Ling, Bingo Wing-Kuen Wu, Yaopan Li, Jiao Guangdong University of Technology School of Computer Science and Technology Guangzhou 510006 China Shenzhen Polytechnic University School of Artificial Intelligence Shenzhen 518055 China Faculty of Applied Sciences of the Macao Polytechnic University 999078 Macao Faculty of Science and Technology of the University of Macau 999078 Macao Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou 510006 China Guangdong University of Technology School of Information Engineering Guangzhou 510006 China Sun Yat-sen University Cancer Center Guangdong Provincial Clinical Research Center for Cancer Department of Medical Imaging State Key Laboratory of Oncology in South China Guangzhou 510060 China

The predictive analysis of disease indicators for breast tumor patients holds significant clinical relevance for physicians in diagnosis and treatment, and can benefit from the integration of consumer health technologies in smart healthcare systems. In clinical practice, vast amounts of monitoring data—such as mammography, breast ultrasound, and magnetic resonance imaging—are generated. Smart healthcare devices and remote monitoring systems facilitate the collection, sharing, and utilization of this data, supporting personalized healthcare. In this context, the selection and utilization of medical data features is a crucial task. While some models have demonstrated effectiveness in this area, they often suffer from limitations such as inadequate feature enhancement, insufficient cross-modal interaction, and poor generalization across diverse datasets. To address these issues, we propose an Integrated Progressive Gated Refinement Network (IPGRN). IPGRN is realized through the fusion of the Progressive Inheritance Shared (PIS) module with the Higher-Dimensional Hierarchical Gated Interactions (HD-HGI) module. This deep fusion network, employing a dual-path architecture, demonstrates outstanding performance in multimodal medical multi-index prediction, while exhibiting strong generalization capabilities. We present experimental results on five authentic datasets, empirically confirming the effectiveness of our proposed IPGRN. © 1975-2011 IEEE.

关键词： Breast tumor Gated convolutions Multimodal feature enhancement Radiomics

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Efficient voltage regulation: An RW-ARO optimized cascaded controller approach

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 9卷

作者： Eker, Erdal Izci, Davut Ekinci, Serdar Migdady, Hazem Zitar, Raed Abu Abualigah, Laith Vocational School of Social Sciences Muş Alparslan University Department of Computer Engineering Batman University Applied Science Research Center Applied Science Private University CSMIS Department Oman College of Management and Technology Faculty of Engineering and Computing Liwa College Computer Science Department Al al-Bayt University MEU Research Unit Middle East University Jadara Research Center Jadara University Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk School of Engineering and Technology Sunway University Malaysia

This work introduces novel advancements in automatic voltage regulator (AVR) control, addressing key challenges and delivering innovative contributions. The primary motivation lies in enhancing AVR performance to ensure stable and reliable voltage output. A crucial innovation in this work is the introduction of the random walk aided artificial rabbits optimizer (RW-ARO). This novel optimization strategy incorporates a random walk approach, enhancing the efficiency of AVR control schemes. The proposed cascaded RPIDD²-PI controller, fine-tuned using the RW-ARO, stands out as a pioneering approach in the AVR domain. It demonstrates superior stability, faster response times, enhanced robustness, and improved efficiency compared to existing methods. Comparative analyses with established controller approaches reaffirm the exceptional performance of the proposed method. The new approach results in shorter rise times, quicker settling times, and minimal overshoot, highlighting its effectiveness and speed in achieving desired system responses. Moreover, the novel approach attains higher phase and gain margins, showcasing its superior performance in the frequency domain. The disturbance rejection and harmonic analysis are performed in order to demonstrate the efficacy of the proposed approach for potential real-world applications. The latter analyses further cement the superior capability of the proposed approach for the automatic voltage regulation. © 2024 The Author(s)

关键词： artificial rabbits optimization algorithm Automatic voltage regulator Cascaded RPIDD2-PI controller Optimization Random Walk strategy

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3D CT Liver Images Lesion Extraction and Classification for Using 3D-CNN with GLRLM

3D CT Liver Images Lesion Extraction and Classification for ...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： S. Jayarathna R. Sowmiya G. Nallasivan G. Sahaana S. Ashok R. Saravanakumar Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed tomography images, we were able to categorize aggressive tumors that were found in the liver for the aim of this research. When it comes to the three-dimensional (3D) CT image, the dimension reduction is accomplished by the use of principal component analysis (PCA). The Random Forest Algorithm is responsible for identifying the abnormal hepatic region, and a median filter is used to reduce the amount of noise occurring. Methods of data augmentation are also employed in this process. To extract Gray Level Run Length Matrix (GLRLM) features, using Pyradiomics python package. These features are then fed into a 3D-CNN classifier to classify malignant tumors and haemangiomas. Our model was found to have greater performance after being compared to the findings of other models. This was revealed after the comparison was made. For the training dataset, our model has an accuracy of 97.2%, and for the test dataset, it has an accuracy of 98.25%. This accuracy is determined by the performance metrics that our model incorporates.

关键词： Training Solid modeling Accuracy Three-dimensional displays Computed tomography Liver Feature extraction data augmentation data models Principal component analysis

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Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility

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Geomatics, Natural Hazards and Risk 2023年第1期14卷

作者： Janizadeh, Saeid Bateni, Sayed M. Jun, Changhyun Im, Jungho Pai, Hao-Thing Band, Shahab S. Mosavi, Amir Department of Civil and Environmental Engineering and Water Resources Research Center University of HI at Manoa HonoluluHI United States Department of Civil and Environmental Engineering College of Engineering Chung-Ang University Seoul Korea Republic of Department of Urban & Environmental Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Environmental Resources Engineering State University of NY College of Environmental Science and Forestry SyracuseNY United States International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Future Technology Research Center College of Future National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Informatics J. Selye University Komarno Slovakia

In this study, the generalized linear model (GLM) and four ensemble methods (partial least squares (PLS), boosting, bagging, and Bayesian) were applied to predict forest fire hazard in the Chalus Rood watershed in the Mazandaran Province, Iran. data from 108 historical forest fire events collected through field surveys were applied as the basis of the analysis. About 70% of the data were used for training the models, while the remaining 30% was used for testing. A total of 14 environmental, climatic, and vegetation variables were used as input features to the models to predict forest fire probability. After conducting a multicollinearity test on the independent variables, the GLM and the ensemble models were applied for modeling. The efficiency of the models was evaluated using receiver operating characteristic (ROC) curve parameters. Results from the validation process, based on the area under the ROC curve (AUC), showed that the GLM, PLS-GLM, boosted-GLM, Bagging-GLM, and Bayesian-GLM models had efficiencies of 0.79, 0.75, 0.81, 0.84, and 0.85, respectively. The results indicated that all ensemble methods, except the PLS algorithm, improved the performance of the GLM model in modeling forest fire hazards in the Chalus Rood watershed, with the Bayesian algorithm being the most efficient method among them. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Watersheds

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Retraction note: Blockchain multi-objective optimization approach-enabled secure and cost-efficient scheduling for the Internet of Medical Things (IoMT) in fog-cloud system (Soft Computing, (2022), 26, 13, (6429-6442), 10.1007/s00500-022-07167-9)

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Soft Computing 2024年第2期28卷 921-921页

作者： Lakhan, Abdullah Mohammed, Mazin Abed Elhoseny, Mohamed Alshehri, Mohammad Dahman Abdulkareem, Karrar Hameed Department of Computer Science Dawood University of Engineering and Technology Sindh Karachi74800 Pakistan College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China College of Computer Science and Information Technology University of Anbar Mohamed Elhoseny Anbar31001 Iraq College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia College of Agriculture Al-Muthanna University Samawah66001 Iraq

The publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher no longer has confidence in the results and conclusions of this article. Mazin Abed Mohammed has stated on behalf of all authors that all authors disagree with this retraction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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The International Monetary Funds intervention in education systems and its impact on childrens chances of completing school

arXiv

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arXiv 2021年

作者： Daoud, Adel Institute for Analytical Sociology Department of Management and Engineering Linköping University Norrköping Sweden The Division of Data Science and Artificial Intelligence The Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Enabling children to acquire an education is one of the most effective means to reduce inequality, poverty, and ill-health globally. While in normal times a government controls its educational policies, during times of macroeconomic instability, that control may shift to supporting international organizations, such as the the International Monetary Fund (IMF). While much research has focused on which sectors has been affected by IMF policies, scholars have devoted little attention to the policy content of IMF interventions affecting the education sector and children's education outcomes: denoted IMF-education policies. This article evaluates the extent which IMF-education policies exist in all programs and how these policies and IMF programs affect children's likelihood of completing schools. While IMF-education policies have a small adverse effect yet statistically insignificant on children's probability of completing school, these policies moderate effect heterogeneity for IMF programs. The effect of IMF programs (joint set of policies) adversely effect children's chances of completing school by six percentage points. By analyzing how IMF-education policies but also how IMF programs affect the education sector in low- and middle-income countries, scholars will gain a deeper understanding of how such policies will likely affect downstream outcomes. © 2021, CC BY-NC-ND.

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Fashionable Four Wheeler Parking to Reduce the Human Intervention and to Enhance the Flexibility in Parking System Using IoT

Fashionable Four Wheeler Parking to Reduce the Human Interve...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Manoj P Varun C M Keerthi Kumar N Pradip Padhye K. Aruna Kumari Puja Das Swagata Sarkar Department of Civil Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Science and business system R.M.K. Engineering College Kavaraipettai India Department of Mechanical Engineering BMS Institute of Technology and Management Bengaluru India School of Management Dr. Vishwanath Karad MIT World Peace University Pune India Department of Mathematics Gitam University Visakhpatanam India Department of Computer science Hiralal Mazumder Memorial college for Women Kolkata India Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India

Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform.

关键词： Space vehicles Technological innovation Motor drives Cloud computing Costs Logic gates Sensor systems

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Knowledge Enhancement using Question Generation for Images and Chart data Input

Knowledge Enhancement using Question Generation for Images a...

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artificial intelligence and Speech technology (AIST), International Conference on

作者： Devendra Kumar Tayal Amita Jain Nikita Shrivastava Akshita Jain Hunny Gaur Department of Computer Science & Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Computer Science & Engineering Netaji Subhash University of Technology Delhi India Department of Artificial Intelligence & Data Science Indira Gandhi Delhi Technical University for Women Delhi India Department of Computer Science St. Stephen’s College University of Delhi Delhi India

In the current era, pictorial representation of data in the form of patterns, charts, graphs and trends are gaining a lot of attention in the fields of finance and science. Deep and correct study of the trends going on is necessary to better one's financial and general knowledge and helps reading and comparing the patterns over a period of time. Correct understanding of the pictorial representation of data in the form of line charts, bar charts or any textual representation is a major task. Hence, the proposed method aims at reading these charts and images, converting them into text and generating questions and then using sentiment analysis to determine positive or negative trends. This is followed by generating commonly asked questions based on the extracted text and providing more context and information on this text using web crawling, which helps provide greater dimensionality and understanding of the problem at hand.

关键词： Sentiment analysis Finance Speech enhancement Market research data mining Task analysis artificial intelligence

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Audio is all in one: speech-driven gesture synthetics using WavLM pre-trained model

arXiv

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arXiv 2023年

作者： Zhang, Fan Ji, Naye Gao, Fuxing Zhao, Siyuan Wang, Zhaohan Li, Shunman The Faculty of Humanities and Arts Macau University of Science and Technology China The College of Media Engineering Communication University of Zhejiang China Research Center for Artificial Intelligence and Fine Arts Zhejiang Lab Zhejiang China The School of Animation and Digital Arts Communication University of China Beijing China Zhejiang Institute of Economics and Trade Zhejiang China

The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has made progress by using acoustic and semantic information as input and adopting classify method to identify the person’s ID and emotion for driving co-speech gesture generation. However, this endeavour still faces significant challenges. These challenges go beyond the intricate interplay between co-speech gestures, speech acoustic, and semantics;they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces "diffmotionv2," a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with WavLM pre-trained model. It can produce individual and stylized full-body co-speech gestures only using raw speech audio, eliminating the need for complex multimodal processing and manually annotated. Firstly, considering that speech audio not only contains acoustic and semantic features but also conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract low-level and high-level audio information. Secondly, we introduce an adaptive layer norm architecture in the transformer-based layer to learn the relationship between speech information and accompanying gestures. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm the WavLM and the model’s ability to synthesize natural co-speech gestures with various styles. © 2023, CC BY.

关键词： Classification (of information)

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