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Touching the limit of Rolling Multilayer Perceptron for efficient two-dimensional medical image segmentation

ENGINEERING applications OF artificial INTELLIGENCE 2025年 153卷

作者： Liu, Yutong Zhu, Haijiang An, Ning Xu, Li Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China China Coal Res Inst Res Inst Mine Artificial Intelligence Beijing 100013 Peoples R China State Key Lab Intelligent Coal Min & Strata Contro Beijing 100013 Peoples R China Capital Med Univ Beijing Jishuitan Hosp Dept Radiol Beijing 100035 Peoples R China

Significant progress has been made in medical image segmentation using deep learning techniques, with the Ushaped architecture being a classic choice. However, effectively capturing and integrating both local features and remote dependencies remains a key challenge for improving deep learning-based segmentation methods. In this paper, we propose a flexible Rolling Multilayer Perceptron (Rolling-MLP) module to address this issue. Building upon this concept, we present the Rolling-Unet network, which combines the strengths of Multilayer Perceptrons (MLPs) with Convolutional neural networks (CNNs) to efficiently extract and fuse local features and remote dependencies. Furthermore, to explore the potential of Rolling-MLP for two-dimensional medical image segmentation, we propose Rolling-MLP configurations with distinct receptive field shapes (linear and area-shaped) and summarize the influence of Rolling-MLP's key parameters on the shape of receptive fields. We conducted extensive experiments on four datasets, surpassing a variety of state-of-the-art methods in accuracy. Moreover, Rolling-MLP is far ahead in Central processing Unit (CPU) inference speed, indicating its potential in medical cyber-physical systems engineering applications. This paper demonstrates the strong comprehensive ability of Rolling-MLP in two-dimensional medical image segmentation tasks, providing a novel approach for constructing medical image segmentation networks, alternative to CNNs and Transformers.

关键词： Cyber-physical systems engineering application Medical image segmentation Multilayer perceptron Remote dependency

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Systematic literature review of AI algorithms applied to unmanned aerial vehicle images

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INTERNATIONAL JOURNAL OF image AND DATA FUSION 2025年第1期16卷

作者： El Khadir, Kenza Ait Fadil, Abdelhamid El Brirchi, El Hassan Hassania Sch Publ Works LaGes Lab Km 7 Jadida RdBP 8108 Casablanca Morocco

artificial Intelligence (AI) combined with image processing has shown significant improvements through new techniques such as Machine Learning (ML) models. This paper introduces the key methods and algorithms used for Drone image processing. We discuss the benefits and limitations of using ML models instead of classical techniques. Our goal is to classify, categorize and describe the methods that are used in realistic settings of diverse domains of applications. We conducted a systematic literature review where systems presented in the papers were analysed based on their domain, task, technology, and efficiency. By extensively reviewing the existing literature, we successfully identified key themes and trends that emerged across the various research questions. The overall findings of the research emphasise the potential of AI and drone imagery in numerous fields. However, the review also uncovered several challenges that necessitate attention, such as issues related to data quality and the requirement for more advanced AI algorithms. The paper outlines significant innovations in the field and offers recommendations for future research directions. By highlighting cross-disciplinary insights, it delves into methodological approaches, exploring commonalities in AI algorithms and UAVs technologies.

关键词： artificial intelligence ML image processing computer vision UAVs (unmanned aerial vehicles) drones image recognition fault detection object detection inspection agriculture environmental pollution CNNs (convolutional neural networks) infrastructure

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Dual-function switchable terahertz metamaterial device assisted by a deep neural network

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2025年第4期42卷 529-541页

作者： Ren, Jiali Zhang, Ting Li, Jing Shanxi Vocat Univ Engn Sci & Technol Sch Comp Engn Jinzhong 030619 Peoples R China Shanxi Vocat Univ Engn Sci & Technol Sch Informat Engn Jinzhong 030619 Peoples R China North Univ China Sch Instrument & Elect Taiyuan 030051 Peoples R China

A dual-functional switchable terahertz metamaterial (THz MM) device with broadband absorption and polarization conversion capabilities is proposed, leveraging the insulator-to-metal phase transition of vanadium dioxide (VO2). In its metallic state, the device functions as an absorber, achieving a bandwidth of 3.93 THz within the frequency range of 2.53-6.46 THz and maintaining an absorption rate of >= 90%. In its insulating state, the device operates as a polarization converter, providing two conversion modes: linear-to-cross-polarization (LTX) over the range of 1.17-3.63 THz, and linear-to-circular polarization (LTC) within the ranges of 1.04-1.14 THz and 3.71-3.87 THz. This study presents a significant advancement over conventional THz devices by overcoming the limitations of single-functionality through innovative design strategies and offering what we believe is a novel approach to multifunctional integration. Leveraging the switchable properties of VO2, the device achieves outstanding performance in both broadband absorption and polarization conversion. Furthermore, deep neural networks (DNNs) significantly reduce the computational time required by traditional optimization methods by efficiently navigating multi-dimensional parameter spaces. DNNs can also capture complex nonlinear relationships between parameters that are often difficult to model with conventional techniques. This highlights the potential of artificial intelligence in the design of functional materials. With these capabilities, the device shows promising applications in fields such as THz stealth technology, energy harvesting, THz communication, polarization imaging, and optical information processing. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Anderson localization Information processing Metamaterial devices Mode conversion neural networks Polarimetric imaging

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A Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing 5th

A Comprehensive Analysis for Advancements and Challenges in ...

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5th International Conference on Data Science, Machine Learning and applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. The rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional neural networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning image processing Backpropagation algorithm Convolutional neural networks (CNNs) Model structures

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Deep spiking neural networks based on model fusion technology for remote sensing image classification

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 142卷

作者： Niu, Li-Ye Wei, Ying Zhao, Liping Hu, Keli Zhejiang Chinese Med Univ Zhejiang Prov Hosp Chinese Med Dept Radiol Affiliated Hosp 1 Hangzhou 310006 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shaoxing Univ Dept Comp Sci & Engn Shaoxing 312000 Peoples R China

The spiking neural network (SNN) based on brain inspiration, as the third-generation neural network, has attracted great research interest due to its ultra-low power event-driven data processing method. How to obtain high-accuracy deep networks has always been a challenge in the field of SNN. At present, there are two main methods for training deep spiking neural networks (SNNs). The first is the spike-based spatiotemporal backpropagation (STBP), and the other indirect is to convert the trained artificial neural network (ANN) into SNN (ANN-SNN). Algorithms that directly train SNNs are usually inefficient, while the ANN-SNN-based models require along inference time and also suffer from performance losses. The model fusion technology (MFT) proposed in this paper, combined with ANN-SNN and STBP, provides a new training paradigm for obtaining deep SNNs. We propose a bilateral multi-strength integrate-and-fire (BM-IF) spiking neuron for ANN-SNN to simplify the conversion operation. Under the same network architecture and encoding time window, the ResNet34 based on our algorithm achieved State-of-the-art (SOTA) in imageNet. At the same time, we combined transfer learning methods to achieve SOTA results on three remote sensing scene classification datasets. Our results also indicate that the MFT-based SNN outperforms the optimal inference accuracy provided by ANNSNN in terms of network architecture and dataset inference time steps reduced by 512-1250 times. It can be seen that this algorithm provides a low-latency and high-precision training scheme for the development of deep SNN.

关键词： Spiking neural network Model fusion technology Low-latency High-precision Transfer learning

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The State of Retinal image Analysis: Deep Learning Advances and applications

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IEEE ACCESS 2025年 13卷 65066-65093页

作者： Soomro, Toufique Ahmed Bin Ubaid, Faisal Zardari, Shehnila Shah, Ahmed Ali Alansari, Marwah M. Nagro, Shimaa A. Charles Univ Prague Artificial Intelligence & Cyber Futures Inst Bathurst NSW 2795 Australia Univ Larkano Dept Elect Engn Larkana 77280 Pakistan Sukkur IBA Univ Comp Sci Dept Sukkur 65200 Pakistan NED Univ Engn & Technol Dept Software Engn Karachi 75270 Pakistan Sukkur IBA Univ Elect Engn Dept Sukkur 65200 Pakistan Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

This study explores deep learning techniques that can help diagnose eye conditions, focusing on diabetic retinopathy (DR). By using cutting-edge technology in image processing and artificial intelligence, it aims to better understand and classify various eye issues like microaneurysms, hemorrhages, and others. Despite progress, challenges like poor image quality and noise persist. The paper stresses the need for improved methods to detect small blood vessels while reducing interference. It also addresses the complexity of uneven lighting in extracting blood vessel images from the retina. The main contribution of this study is its thorough examination of existing methods and its detailed investigation into using deep learning for detecting retinal blood vessels. It highlights the effectiveness of different deep learning models and their potential in accurately classifying DR grades. This research underscores the importance of grading methods in analyzing medical images, especially retinal images, to enhance the accuracy of diagnosing eye diseases.

关键词： Retina Biomedical imaging Blood vessels Feature extraction Accuracy image segmentation Hemorrhaging Deep learning Classification algorithms Retinal vessels Diabetic retinopathy retinal blood vessels color fundus images deep learning microaneurysms hemorrhages exudates image processing techniques convolutional neural networks medical image analysis

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Energy-Efficient, Delay-Constrained Edge Computing of a Network of DNNs

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IEEE TRANSACTIONS ON COMPUTERS 2025年第2期74卷 569-581页

作者： Ghasemi, Mehdi Heidari, Soroush Kim, Young Geun Wu, Carole-Jean Vrudhula, Sarma Arizona State Univ Tempe AZ 85281 USA Korea Univ Seoul 02841 South Korea Meta Inc Cambridge MA 02142 USA

This paper presents a novel approach for executing the inference of a network of pre-trained deep neural networks (DNNs) on commercial-off-the-shelf devices that are deployed at the edge. The problem is to partition the computation of the DNNs between an energy-constrained and performance-limited edge device EE, and an energy-unconstrained, higher performance device CC, referred to as the cloudlet, with the objective of minimizing the energy consumption of EE subject to a deadline constraint. The proposed partitioning algorithm takes into account the performance profiles of executing DNNs on the devices, the power consumption profiles, and the variability in the delay of the wireless channel. The algorithm is demonstrated on a platform that consists of an NVIDIA Jetson Nano as the edge device EE and a Dell workstation with a Titan Xp GPU as the cloudlet. Experimental results show significant improvements both in terms of energy consumption of EE and processing delay of the application. Additionally, it is shown how the energy-optimal solution is changed when the deadline constraint is altered. Moreover, the overhead of decision-making for our proposed method is significantly lower than the state-of-the-art Integer Linear Programming (ILP) solutions.

关键词： Flow graphs Performance evaluation Delays Computational modeling Servers artificial neural networks Energy efficiency Energy consumption Data models image edge detection Deep neural networks IoT edge energy efficiency

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A deep learning framework for solving fractional partial differential equations

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PHYSICA SCRIPTA 2025年第4期100卷 046012-046012页

作者： Ali, Amina Senu, Norazak Ahmadian, Ali Wahi, Nadihah Univ Putra Malaysia Dept Math & Stat UPM 43400 Serdang Malaysia Univ Sulaimani Coll Educ Dept Math Sulaymaniyah Iraq Univ Putra Malaysia Inst Math Res UPM 43400 Serdang Malaysia Jadara Univ Jadara Univ Res Ctr Jadara Jordan Istanbul Okan Univ Fac Engn & Nat Sci Istanbul Turkiye

This research focuses on the study and solution of fractional partial differential equations (FPDEs), a critical area in mathematical analysis. FPDEs pose significant challenges due to their complexity, often requiring extensive computational resources to solve. Given the scarcity of exact solutions, numerical methods have been a primary approach for tackling FPDEs. However, these methods often yield substantial but limited results. The ongoing quest for more effective solutions has led researchers to explore new methodologies. Recent advancements in deep learning (DL), particularly in deep neural networks (DNNs), offer promising tools for solving FPDEs due to their exceptional function-approximation capabilities, demonstrated in diverse applications such as image classification and natural language processing. This research addresses the challenges of solving FPDEs by proposing a novel deep feedforward neural network (DFNN) framework. The method integrates the Laplace transform for memory-efficient Caputo derivative approximations and demonstrates superior accuracy across various examples. The results highlight the framework's versatility and computational efficiency, establishing it as a powerful tool for solving FPDEs.

关键词： laplace transform method fractional partial differential equations artificial neural networks gradient descent deep neural network

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Automatic inverse design of second-order differential metasurfaces based on reinforcement learning

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 153卷

作者： Li, Zhaohui Yang, Shuo Liu, Delian Xian Univ Sci & Technol Coll Commun & Informat Technol Xian 710054 Peoples R China Xian Inst Appl Opt Xian 710065 Peoples R China Xidian Univ Sch Optoelect Engn Xian 710071 Peoples R China

Second-order differential metasurfaces, as two-dimensional artificial nanostructures, hold great potential for various applications in optical computing, image processing, microscopy imaging, etc. They have the capability to perform second-order mathematical operations on incident optical fields. In general, the design of second-order differential optical processing metasurfaces needs to consider various constraints, including incident optical fields, material properties, and application requirements. This task is challenging and time-consuming, typically requiring manual intervention. Therefore, a reinforcement learning-based artificial intelligence algorithm is proposed for the automatic design of second-order differential metasurfaces. It can rapidly find the desired result by leveraging the powerful self-learning ability of reinforcement learning. This paper first introduces the basic principles of designing second-order differential optical processing metasurfaces. Then, it presents a new automatic design method for second-order differential metasurfaces based on reinforcement learning. Finally, a design of a second-order differential metasurface is presented. Simulation and experimental results validate the effectiveness of the proposed method. The proposed method establishes a basic framework for applying reinforcement learning to the automatic design of photonic devices and outlines the various constraint factors that need to be coupled and considered during the design process. A surrogate model based on neural networks is presented to fit the transmission calculated by finite difference time domain software. A new reward formula is given to sufficiently consider the transmission efficiency of whole devices. The proposed method effectively leverages the self-learning capability of reinforcement learning, circumventing challenges related to manual intervention, computational complexity, and time-consuming tasks in conventional methods.

关键词： Second-order differential metasurface Automatic inverse design artificial intelligence Reinforcement learning Multi-layer perceptron

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Neuromorphic devices assisted by machine learning algorithms

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INTERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 042007-042007页

作者： Huo, Ziwei Sun, Qijun Yu, Jinran Wei, Yichen Wang, Yifei Cho, Jeong Ho Wang, Zhong Lin Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Engn Beijing 100049 Peoples R China Guangxi Univ Ctr Nanoenergy Res Sch Phys Sci & Technol Nanning 530004 Peoples R China Shandong Zhongke Naneng Energy Technol Co Ltd Dongying Peoples R China Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea Georgia Inst Technol Atlanta GA 30332 USA

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided. The review discusses the basic structure of simple neuron models inspired by biological neurons and how they process information in simple neural networks, laying the foundation for neuromorphic device *** progress in the fabrication of neuromorphic devices is highlighted, focusing on advancements in materials, structures, and the development of st

关键词： neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors

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