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Image segmentation via two-step deep variational priors

Pattern Recognition Letters 2025年 195卷 44-50页

作者： Tan, Lu Tai, Xue-Cheng Li, Ling Liu, Wan-Quan Chan, Raymond H. Hong, Dan-Feng School of Computer Science and Information Engineering Bengbu University Bengbu Anhui China Norwegian Research Center (NORCE) Bergen Norway School of Elec Eng Comp and Math Sci (EECMS) Curtin University Perth Australia School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Operations and Risk Management and School of Data Science Lingnan University Tuen Mun Hong Kong Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

This paper proposes an iterative deep variational approach for image segmentation in a fusion manner: it is not only able to realize selective segmentation, but can also alleviate the issue of parameter/initialization dependency. Moreover, it possesses a refinement process designed to handle challenging scenarios, such as images containing obscured, damaged, or absent objects, or those with complex backgrounds. Our proposed approach consists of two main procedures, i.e., selective segmentation and shape transformation. The first procedure works as a stem in a totally unsupervised way. A convolutional neural network (CNN) based architecture is properly incorporated into the selective weighting constrained variational segmentation model. The second procedure is to further refine the outputs. This part can be achieved in two ways: one direction is to establish a joint model with the semantic shape constraint. The other technical direction is to make the shape descriptor separated from the joint model and work as an individual unit. In the proposed approach, the minimization problem is transformed from iterative minimization for each variable to automatically minimizing the loss function by learning the generator network parameters. This also leads to a good inductive bias associated with classic variational methods. Extensive experiments have demonstrated the significant advantages. © 2025 Elsevier B.V.

关键词： Flexible module Integration approach Iterative deep variational priors

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Adisutjipto Institute of aerospace Technology Virtual Tour with Artificial Intelligence (AI) Agent 1

Adisutjipto Institute of Aerospace Technology Virtual Tour w...

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1st International Conference of Adisutjipto on aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Ayuningtyas, Astika Wahyudi Sumari, Arwin Datumaya Aryanto, Salam Nuryatno, Edi Triono Mulyani, Sri Kusumaningrum, Anggraini Adisutjipto Institute of Aerospace Technology Aerospace Intelligent Systems and Data Engineering Research Group Faculty of Industrial Technology Yogyakarta Indonesia Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Modeling and Simulation Research Group Faculty of Industrial Technology Yogyakarta Indonesia The University of Western Australia Uwa Centre for Medical Research Perth Australia Adisutjipto Institute of Aerospace Technology Faculty of Aerospace Engineering Technology Yogyakarta Indonesia

ISBN: (纸本)9798331507930

This study aims to develop an interactive virtual tour based on Android with a first-person shooter approach to introduce the Adisutjipto Institute of aerospace Technology (ITD Adisutjipto) virtually. This virtual tour utilizes an Artificial Intelligence (AI) agent as an intelligent guide who can answer questions and provide relevant information related to facilities and locations inside the ITD Adisutjipto building. The test results show that this virtual tour is effective and feasible to use, with a user satisfaction rate reaching 87.72%. This study contributes to the field of educational virtual technology by offering a more immersive and interactive experience. Users can easily explore the ITD Adisutjipto building virtually and obtain the information they need efficiently. This research is expected to be an innovative solution to introduce the ITD Adisutjipto to wider community, as well as support learning and research activities in the aerospace technology field. © 2024 IEEE.

关键词： Virtual environments

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intelligent aerospace Terminologies Recognition based on Spectrogram using Machine Learning

Intelligent Aerospace Terminologies Recognition based on Spe...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Sitorus, Zest Amborgang Abdie, Syafrudin Adisutjipto Institute of Aerospace Technology Aerospace Intelligent System and Data Engineering Research Group Yogyakarta Indonesia Politeknik Negeri Malang Cognitive Artificial Intelligence Research Group Malang Indonesia Adisutjipto Institute of Aerospace Technology Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Faculty of Industrial Technology Aerospace Avionics System Research Group Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327298

A speech recognition system is an application to recognize spoken words through audio inputs. On the other hand, spectrograms are visual representations of audio signals and effectively represent words numerically. In this research, we developed a spectrogram-based speech recognition system using machine learning's K-Nearest Neighbors (KNN) method to recognize aerospace terminologies, some uncommon for ordinary people. We used 300 audio signal data consisting of two categories, namely 15 spectrogram data for aerospace terminologies and 15 spectrogram data for non-aerospace ones, each taken ten times. By conducting a 70:30 training and testing scheme, strengthened by k-fold cross-validation, the optimum K for KNN is 13. With such value of K, our developed intelligent system for aerospace terminologies recognition based on spectrogram can achieve an accuracy of 75.56% with a precision of 75.56%, recall of 75.56%, and F-1 score of 75.56%. With that accuracy, our developed intelligent system is considered as a good system. © 2023 IEEE.

关键词： Terminology

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Application for Airport Tenant Selection Using Weighted Product-based Decision Support System

Application for Airport Tenant Selection Using Weighted Prod...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Saputra, Muhamad Andika Ayuningtyas, Astika Sudarmadi, Catur Adisutjipto Institute of Aerospace Technology Aerospace Intelligent System and Data Engineering Research Group Yogyakarta Indonesia Politeknik Negeri Malang Cognitive Artificial Intelligence Research Group Malang Indonesia Adisutjipto Institute of Aerospace Technology Informatics Study Program Faculty of Industrial Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Health Informatics Research Group Faculty of Industrial Technology Yogyakarta Indonesia

ISBN: (纸本)9798350327298

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A-1 to A-4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A-4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V-4 is 0.294. © 2023 IEEE.

关键词： Decision support systems

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Theory of Evolutionary systems engineering

Theory of Evolutionary Systems Engineering

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Hickinbotham, Simon Dubey, Rahul Buchanan, Edgar Friel, Imelda Colligan, Andrew Price, Mark Tyrrell, Andy M. Intelligent Systems & Robotics Research Group School of Physics Engineering and Technology University of York United Kingdom School of Mechanical & Aerospace Engineering Queen's University Belfast United Kingdom

ISBN: (纸本)9781665430654

Evolutionary approaches to engineering design involve generating populations of candidate solutions that compete via a selection process iteratively, to improve measures of performance over many generations. Although the attractive properties of biological evolutionary systems have motivated researchers to investigate emulating them for engineering design, there has been an emphasis on using encodings of the technical artefacts themselves, rather than encoding a complete bio-inspired system which is capable of producing such artefacts. It is the latter approach which is the subject of this contribution: how might a bio-inspired system be designed that self-organises the process of engineering design and manufacture? To make progress in the application of evolutionary processes to problems in engineering design, the evolutionary model must encompass the complexity of systems engineering. A new theory of evolutionary systems engineering is presented, based on von Neumann's Universal Constructor Architecture (UCA), drawing from more recent understanding of biology and applying the resulting system to the task of engineering design. It demonstrates how individual bioinspired algorithms fit into a coherent whole, and how they can be combined to drive open-endedness in automated design. The resulting system provides a common language for multidisciplinary applications in generative design, whereby industrial systems engineering approaches can be developed using principles from the UCA for the first time. © 2023 IEEE.

关键词： Bio-inspired Evolvable systems Multi-Agent System

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Investigation of starting conditions in generative processes for the design of engineering structures

Investigation of starting conditions in generative processes...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Buchanan, Edgar Dubey, Rahul Hickinbotham, Simon Friel, Imelda Colligan, Andrew Price, Mark Tyrrell, Andy M. School of Physics Engineering and Technology University of York Intelligent Systems & Robotics Research Group United Kingdom School of Mechanical & Aerospace Engineering Queen's University Belfast United Kingdom

ISBN: (纸本)9781665430654

engineering design has traditionally involved human engineers manually creating and iterating on designs based on their expertise and knowledge. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are used to ex-plore a much larger design space that may not have ever been considered by human engineers. However, for complex systems, the designer is often required to start the EvoDevo process with an initial design (seed) which the development process will optimise. The question is: will a good starting seed yield a good set of design solutions for the given problem? This paper considers this question and suggests that sub-optimal seeds can provide, up to certain limits, better design solutions than relatively more optimal seeds. In addition, this paper highlights the importance of designing the appropriate seed for the appropriate problem. In this paper, the problem analysed is the structural performance of a Warren Truss (bridge-like structure) under a single load. The main conclusion of this paper is that up to a limit sub-optimal seeds provide in general better sets of solutions than more optimal seeds. After this limit, the performance of sub-optimal seed starts to degrade as parts of the phenotype landscape become inaccessible. © 2023 IEEE.

关键词： evodevo generative design genetic algorithms neural networks structural engineering

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4 Dimensional trajectory functionalities for air traffic management systems: Novel flight management system for improved safety and sustainability in the CNS+A context 15

4 Dimensional trajectory functionalities for air traffic man...

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2015 15th Annual Integrated Communication, Navigation and Surveillance Conference, ICNS 2015

作者： Gardi, Alessandro Sabatini, Roberto Kistan, Trevor Lim, Yixiang Ramasamy, Subramanian RMIT University School of Aerospace Mechanical and Manufacturing Engineering Intelligent Transport Systems Research Group - Air Transport/Aviation Technology Team Australia THALES Australia Australia Imperial College London United Kingdom

ISBN: (纸本)9781479989522

?Development of 4-PNV and NG-FMS providing multi-objective 4DT optimisation and air-to-ground negotiation/validation to enable TBO/IBO ?Implementation of gaseous emissions, condensation trails, operational costs, runway capacity and weather/non-standard atmosphere models ?Development of a multi-objective 4DT optimisation algorithm for strategic and tactical online air traffic operations, also including an operational smoothing algorithm for reducing the mathematical CPWS optimum into a flyable and concisely described 4DT ?Demonstrated functional capability to generate cost-effective and environment-friendly trajectory profiles satisfying the operational requirements of typical tactical online tasks ?Future research will expand the uncertainty analysis in detail and will address the integration of systems for cooperative and non-cooperative SAA in the NG-MMS ?Additionally CNS ABIA algorithms will be incorporated as an integral part of both flight and mission management systems for a variety of applications.

关键词： Conceptual design

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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