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28th International Conference on Information processing in Medical Imaging (IPMI)

作者： Wolf, Tom Nuno Poelsterl, Sebastian Wachinger, Christian Tech Univ Munich Dept Radiol Munich Germany Ludwig Maximilians Univ Munchen Lab Artificial Intelligence Med Imaging Munich Germany

ISBN: (纸本)9783031340475;9783031340482

Alzheimer's disease (AD) has a complex and multifactorial etiology, which requires integrating information about neuroanatomy, genetics, and cerebrospinal fluid biomarkers for accurate diagnosis. Hence, recent deep learning approaches combined image and tabular information to improve diagnostic performance. However, the black-box nature of such neural networks is still a barrier for clinical applications, in which understanding the decision of a heterogeneous model is integral. We propose PANIC, a prototypical additive neural network for interpretable AD classification that integrates 3D image and tabular data. It is interpretable by design and, thus, avoids the need for post-hoc explanations that try to approximate the decision of a network. Our results demonstrate that PANIC achieves state-of-the-art performance in AD classification, while directly providing local and global explanations. Finally, we show that PANIC extracts biologically meaningful signatures of AD, and satisfies a set of desirable desiderata for trustworthy machine learning. Our implementation is available at https://***/ai-med/PANIC.

关键词： Cerebrospinal fluid

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Developments in image processing using Deep learning and Reinforcement learning 3

Developments in Image Processing using Deep learning and Rei...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： More, Sarla Mishra, Durgesh Symbiosis University of Applied Sciences Department of It MP Indore India

ISBN: (纸本)9798350343274

The continuously surging volume of data, estimated to surpass 180 zettabytes by 2025, presents substantial challenges for both organizations and society as a whole. This influx of data not only brings about increased size but also introduces heightened complexity, presenting new theoretical and computational obstacles. Over the past two decades, the field of data science has experienced a notable surge in popularity due to its versatile applications in managing complex data, ensuring high accuracy, providing customizable flexibility, and demonstrating excellent adaptability. However, the analysis of image datasets introduces additional challenges, particularly when the goal is to enhance image quality, which in turn increases the volume of data for processing. Despite the continued dominance of traditional machine learning (ML) techniques in various research fields and industries, there is a growing interest among the scientific community in exploring innovative artificial intelligence (AI) approaches. The resurgence of neural networks, in particular, has led to significant breakthroughs, especially in the domains of image understanding and processing. This research conducts an extensive examination of the latest progress in designing and optimizing artificial intelligence (AI) solutions specifically tailored to tackle challenges in image processing. Despite the significant accomplishments, the field continues to grapple with various difficulties. The present study thoroughly explores essential and recent improvements, applications, and advancements within the sphere of image processing, offering insights into a domain characterized by continual and swift evolution. Additionally, the paper delineates prospective avenues for future research in this dynamic field. © 2023 IEEE.

关键词： Reinforcement learning

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Deep Learning Models for Moving Vehicle Detection in Traffic Surveillance Video for Smart City applications 1

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2nd International Conference on Electronic Governance with Emerging Technologies, EGETC 2023

作者： Risha, K. Jude Hemanth, D. Department of ECE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9783031439407

ISBN: (纸本)9783031439391

In the last few decades, CCTV surveillance has become a piece of unavoidable equipment in public places. The most important application is for traffic monitoring. Recently, accident cases have been rapidly increasing day by day. So, continuous analysis of vehicles is a challenging task. Developing an algorithm to find a specific vehicle or pedestrian is highly beneficial in such cases. Such a procedure can be done by video processing. Video processing and its applications have improved over the last few years. The advancements in this area are helpful in numerous fields. At the same time, the intervention of artificial intelligence also has a relevant role in this area. Machine learning has also become essential and valuable in various complex cases. image classification using artificial intelligence provides a highly accurate result rather than any other classification method. This paper mainly compares different convolutional neural networks on moving objects detected by background subtraction and GMM with the morphological operation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Profiling Language Sign Using Deep Learning 10

Profiling Language Sign Using Deep Learning

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10th International Conference on Wireless networks and Mobile Communications, WINCOM 2023

作者： Elbaghazaoui, Bahaa Eddine Amnai, Mohamed Fakhri, Youssef Choukri, Ali Gherabi, Noreddine IbnTofail University Laboratory of Computer Sciences Faculty of Sciences Kenitra Kenitra Morocco Sultan Moulay Slimane University National School of Applied Sciences Khouribga Morocco

ISBN: (纸本)9798350329674

Large segments of society place a high value on sign language as a distinct communication language. In computer vision, gesture detection is still a developing phenomenon and a hot topic. Aside from its many applications in immersive game technology and human-computer interaction, gesture recognition has many applications in deaf-mute sign language interpretation. In order to profile language signs, we created a model of image processing that recognizes Arabic hand sign language based on artificial neural networks, starting with the data collection process and continuing with data profiling, then using the deep learning model to train and identify gestures, all the way to the presentation of the results. We offer a method to help with the conversion of sign language into voice and text-based formats. The outcomes we have achieved are crucial, important and significant for our next achievements. © 2023 IEEE.

关键词： neural networks

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Convolutional neural networks for Engineering Design Validation

Convolutional Neural Networks for Engineering Design Validat...

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applications of Machine Learning 2024

作者： Lowandy, K. Kelliher, S. Le, D. Molinari, C. Harris, I. Unger, L. Fink, R. Shemelya, C. Robinette, P. University of Massachusetts Lowell Raytheon UMass Lowell Research Institute LowellMA United States Applied NanoTech Inc United States

ISBN: (纸本)9781510679368

The growth of artificial intelligence has led to the widespread use of convolutional neural networks (CNNs) for computer vision applications, traditionally for binary and categorical classification tasks. However, there remains untapped potential for advancing computer vision through deep learning in regression tasks. Design engineers across many disciplines use computer-aided design software to model their designs. These computer-integrated designs often require machinery for construction or fabrication. For many engineering designs, precision and tolerancing is essential for the proper function and performance of the design. The engineering process typically involves manual testing and parameter measurements to ensure the proper function of the design before it is marketed. However, training a neural network to automate these tests and provide accurate numeric estimates of system parameters without manual intervention can significantly increase efficiency and decrease the time to market for many products. This shift from manual to automated testing allows for a heightened focus on innovation and project development while minimizing the time and resource dedication for validation. This article outlines the implementation of CNN models designed to enhance the efficiency of manually validating engineered projects. Our approach involves utilizing computer-aided design simulation image captures as training data for our pipeline. We integrate a real-time color-filtering and fiducial rotation scaling normalization process on any fabricated design image. Through these pre-processing methods, our algorithm can perceive these images in a consistent manner with simulation images from the model training. Our current model is trained with only 1020 simulation images and achieves a 1.99% average training prediction error on this dataset after training. Before, our errors were a 10.51% average error in our initial model implementation and 3.63% in our second implementation. On o

关键词： Precision engineering

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Bio-Inspired Electronic Eyes and Synaptic Photodetectors for Mobile artificial Vision

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2022年第2期1卷 76-87页

作者： Choi, Changsoon Seung, Hyojin Kim, Dae-Hyeong Seoul02792 Korea Republic of Seoul08826 Korea Republic of School of Chemical and Biological Engineering Institute of Chemical Processes Seoul National University Seoul08826 Korea Republic of

Conventional imaging and data processing devices are not ideal for mobile artificial vision applications, such as vision systems for drones and robots, because of the heavy and bulky multilens optics in the camera modules. Furthermore, the physically isolated image data processing units of conventional systems induce large power consumption and data latency. For mobile artificial vision applications, electronic eyes, including neuromorphic ones, have been developed inspired by biological eyes and neural networks. Here, we summarize the development of such bio-inspired electronic eyes and synaptic photodetectors (PDs). Bio-inspired electronic eyes, typically consisting of curved image sensor arrays, enable aberration-free imaging and module size miniaturization in addition to other advantageous optical features, such as wide field-of-view and deep depth-of-field. Furthermore, photodetecting devices with synaptic properties can efficiently enhance image contrast because of photon-triggered synaptic plasticity. Therefore, the signal-to-noise ratio of the acquired image can be enhanced, which facilitates efficient image recognition for machine vision. A brief summary of the remaining challenges and prospects concludes this review. © 2022 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Cameras

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Medical Transformers for Boosting Automatic Grading of Colon Carcinoma in Histological images 1

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22nd International Conference on image Analysis and processing (ICIAP)

作者： Carcagni, Pierluigi Leo, Marco Signore, Luca Distante, Cosimo CNR ISASI Ecotekne Campus via Monteroni I-73100 Lecce Italy Univ Salento Via Monteroni I-73100 Lecce Italy

ISBN: (数字)9783031431487

ISBN: (纸本)9783031431470;9783031431487

Developing computer-aided approaches for cancer diagnosis and grading is receiving an uprising demand since this could take over intra- and inter-observer inconsistency, speed up the screening process, allow early diagnosis, and improve the accuracy and consistency of the treatment planning processes. The third most common cancer worldwide and the second most common in women is ColoRectal Cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Automatic systems have the potential to speed up and make it more robust but, unfortunately, the most recent and promising machine learning techniques have not been applied for automatic CRC grading so far. For example, there is no work exploiting transformer networks, which outperform convolutional neural networks (CNN) and are replacing them in many applications, for CRC detection and grading at a large scale. To fill this gap, in this work, a transformer-based network endowed with an additional control mechanism in the self-attention module is exploited to understand discriminative regions in large histological images. These relevant regions have been used to train the most suited Convolutional neural Network (as emerged from recent research findings) for the automatic grading of CRC. The experimental proofs on the largest publicly available CRC dataset demonstrated marked improvement with respect to the leading state-of-the-art approaches relying on CNN.

关键词： Colon carcinoma artificial intelligence Deep learning Transformer networks Histological diagnosis

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Comparison of image processing techniques for defect detection 1

Comparison of image processing techniques for defect detecti...

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1st International Workshop of Young Scientists on artificial Intelligence for Sustainable Development, AISD 2024

作者： Kovalskyi, Semen Koval, Vasyl West Ukrainian National University 11 Lvivska Str. Ternopil46009 Ukraine

Defect detection is a crucial quality control process in the manufacturing industry, aimed at identifying and classifying imperfections or anomalies in products before they reach customers. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to human error. This paper provides a comprehensive overview of image-based defect detection algorithms, including traditional image processing techniques, machine learning algorithms, and deep learning models. The study analyzes the strengths, limitations, and performance of each approach across various applications and datasets. The results demonstrate that while traditional methods and machine learning algorithms offer reliable defect detection, deep learning models, particularly convolutional neural networks (CNNs), achieve exceptional accuracy and robustness. However, deep learning models require significant computational resources and large amounts of labeled data for training. The paper highlights the importance of selecting the most appropriate approach based on specific application requirements, data characteristics, and computational constraints. Furthermore, it discusses future research opportunities, such as developing more robust and generalized algorithms, leveraging multi-modal data, improving model interpretability, and enabling real-time and edge computing solutions. © 2024 Copyright for this paper by its authors.

关键词： Computer vision

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An Improved Lane-Keeping Controller for Autonomous Vehicles Leveraging an Integrated CNN-LSTM Approach

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2023年第7期14卷 209-216页

作者： Ngoc, Hoang Tran Hong, Phuc Phan Vinh, Nghi Nguyen Trung, Nguyen Nguyen Nguyen, Khang Hoang Quach, Luyl-Da FPT Univ Software Engn Dept Cantho City Vietnam

Representing the task of navigating a car through traffic using traditional algorithms is a complex endeavor that presents significant challenges. To overcome this, researchers have started training artificial neural networks using data from front-facing cameras, combined with corresponding steering angles. However, many current solutions focus solely on the visual information from the camera frames, overlooking the important temporal relationships between these frames. This paper introduces a novel approach to end-to-end steering control by combining a VGG16 convolutional neural network (CNN) architecture with Long Short-Term Memory (LSTM). This integrated model enables the learning of both the temporal dependencies within a sequence of images and the dynamics of the control process. Furthermore, we will present and evaluate the estimated accuracy of the proposed approach for steering angle prediction, comparing it with various CNN models including the Nvidia classic model, Nvidia model, and MobilenetV2 model when integrated with LSTM. The proposed method demonstrates superior accuracy compared to other approaches, achieving the lowest loss function. To evaluate its performance, we recorded a video and saved the corresponding steering angle results based on human perception from the robot operating system (ROS2). The videos are then split into image sequences to be smoothly fed into the processing model for training.

关键词： End-to-end steering control convolutional neural network LSTM nvidia model MobileNetv2 VGG16

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Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible

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Light(Science & applications) 2022年第7期11卷 1443-1453页

作者： Xuhao Luo Yueqiang Hu Xiangnian Ou Xin Li Jiajie Lai Na Liu Xinbin Cheng Anlian Pan Huigao Duan National Research Center for High-Efficiency Grinding College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China Advanced Manufacturing Laboratory of Micro-Nano Optical Devices Shenzhen Research InstituteHunan UniversityShenzhen 518000China 2nd Physics Institute University of StuttgartPfaffenwaldring 5770569 StuttgartGermany Max Planck Institute for Solid State Research Heisenbergstrasse 170569 StuttgartGermany Greater Bay Area Institute for Innovation Hunan UniversityGuangzhou 511300China

Replacing electrons with photons is a compelling route toward high-speed,massively parallel,and low-power artificial intelligence ***,diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical ***,the existing architectures often comprise bulky components and,most critically,they cannot mimic the human brain for ***,we demonstrate a multi-skilled diffractive neural network based on a metasurface device,which can perform on-chip multi-channel sensing and multitasking in the *** polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable *** areal density of the artificial neurons can reach up to 6.25×10^(6)mm^(-2) multiplied by the number of *** metasurface is integrated with the mature complementary metal-oxide semiconductor imaging sensor,providing a chip-scale architecture to process information directly at physical layers for energy-efficient and ultra-fast image processing in machine vision,autonomous driving,and precision medicine.

关键词： visible autonomous complementary

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