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Single Volume image generator and Deep Learning-Based ASD Classification

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第11期24卷 3044-3054页

作者： Ahmed, Md Rishad Zhang, Yuan Liu, Yi Liao, Hongen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Civil Aviat Gen Hosp Dept Resp Med Beijing 100123 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China

Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Deep learning approaches have been applied in clinical or behavioral identification of ASD;most erstwhile models are inadequate in their capacity to exploit the data richness. On the other hand, classification techniques often solely rely on region-based summary and/or functional connectivity analysis of functional magnetic resonance imaging (fMRI). Besides, biomedical data modeling to analyze big data related to ASD is still perplexing due to its complexity and heterogeneity. Single volume image consideration has not been previously investigated in classification purposes. By deeming these challenges, in this work, firstly, we design an image generator to generate single volume brain images from the whole-brain image by considering the voxel time point of each subject separately. Then, to classify ASD and typical control participants, we evaluate four deep learning approaches with their corresponding ensemble classifiers comprising one amended Convolutional Neural Network (CNN). Finally, to check out the data variability, we apply the proposed CNN classifier with leave-one-site-out 5-fold cross-validation across the sites and validate our findings by comparing with literature reports. We showcase our approach on large-scale multi-site brain imaging dataset (ABIDE) by considering four preprocessing pipelines, which outperforms the state-of-the-art methods. Hence, it is robust and consistent.

关键词： Brain modeling Pipelines Deep learning Functional magnetic resonance imaging Feature extraction generators Biomedical data modeling image generator convolutional neural network (CNN) autism spectrum disorder (ASD) fMRI ABIDE

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Analytic simulator and image generator of multiple-scattering Compton camera for prompt gamma ray imaging

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BIOMEDICAL ENGINEERING LETTERS 2018年第4期8卷 383-392页

作者： Kim, Soo Mee Korea Inst Ocean Sci & Technol Maritime ICT R&D Ctr 385 Haeyang Ro Busan 49111 South Korea

For prompt gamma ray imaging for biomedical applications and environmental radiation monitoring, we propose herein a multiple-scattering Compton camera (MSCC). MSCC consists of three or more semiconductor layers with good energy resolution, and has potential for simultaneous detection and differentiation of multiple radio-isotopes based on the measured energies, as well as three-dimensional (3D) imaging of the radio-isotope distribution. In this study, we developed an analytic simulator and a 3D image generator for a MSCC, including the physical models of the radiation source emission and detection processes that can be utilized for geometry and performance prediction prior to the construction of a real system. The analytic simulator for a MSCC records coincidence detections of successive interactions in multiple detector layers. In the successive interaction processes, the emission direction of the incident gamma ray, the scattering angle, and the changed traveling path after the Compton scattering interaction in each detector, were determined by a conical surface uniform random number generator (RNG), and by a Klein-Nishina RNG. The 3D image generator has two functions: the recovery of the initial source energy spectrum and the 3D spatial distribution of the source. We evaluated the analytic simulator and image generator with two different energetic point radiation sources (Cs-137 and Co-60) and with an MSCC comprising three detector layers. The recovered initial energies of the incident radiations were well differentiated from the generated MSCC events. Correspondingly, we could obtain a multi-tracer image that combined the two differentiated images. The developed analytic simulator in this study emulated the randomness of the detection process of a multiple-scattering Compton camera, including the inherent degradation factors of the detectors, such as the limited spatial and energy resolutions. The Doppler-broadening effect owing to the momentum distribution of

关键词： Multiple-scattering Compton camera Prompt gamma imaging Analytic simulator image generator

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Synthetic image generator for Testing and Training Environments: A Deep Learning Survey

Synthetic Image Generator for Testing and Training Environme...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Venkateswara Reddy, P. Sarasu, P. Kalasalingam Academy of Research and Education Department of Computer Science & Engineering Srivilliputtur Tamil Nadu Krishnankoil626126 India

ISBN: (纸本)9798350313987

image synthesis offers a way to quickly create huge amounts of datasets for regulating the manufacturing process to ensure optimal airflow as well as material while monitoring the generation of data sets variety for machine learning applications. Artificial information leads a vital role along with the training pipeline in the given requirements of deep learning applications. The present study offers an extensive collection of both the modern techniques and image synthesis for visual machine learning which employ for picture synthesizing. image synthesis is described based on such a classification that takes modelling and rendering in the context of image production, and a categorization is also established regarding the computer vision applications which are utilized in. In order to encourage future picture production for machine learning, the designers concentrated on the method's computer graphics components. In the present process, each approach is evaluated for quality and reported performance, by suggesting as to what learning potential is anticipated. The present study acts as thorough reference and directs at both the application development and data production sides. © 2023 IEEE.

关键词： Deep Learning Algorithms image generator Machine Learning Algorithms and image Synthesis

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OmniSCV: An Omnidirectional Synthetic image generator for Computer Vision

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SENSORS 2020年第7期20卷 2066-2066页

作者： Berenguel-Baeta, Bruno Bermudez-Cameo, Jesus Guerrero, Jose J. Univ Zaragoza Inst Invest Ingn Aragon Zaragoza 50018 Spain

Omnidirectional and 360 degrees images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the environment from only an image. However, the distortion of these images requires the development of specific algorithms for their treatment and interpretation. Moreover, a high number of images is essential for the correct training of computer vision algorithms based on learning. In this paper, we present a tool for generating datasets of omnidirectional images with semantic and depth information. These images are synthesized from a set of captures that are acquired in a realistic virtual environment for Unreal Engine 4 through an interface plugin. We gather a variety of well-known projection models such as equirectangular and cylindrical panoramas, different fish-eye lenses, catadioptric systems, and empiric models. Furthermore, we include in our tool photorealistic non-central-projection systems as non-central panoramas and non-central catadioptric systems. As far as we know, this is the first reported tool for generating photorealistic non-central images in the literature. Moreover, since the omnidirectional images are made virtually, we provide pixel-wise information about semantics and depth as well as perfect knowledge of the calibration parameters of the cameras. This allows the creation of ground-truth information with pixel precision for training learning algorithms and testing 3D vision approaches. To validate the proposed tool, different computer vision algorithms are tested as line extractions from dioptric and catadioptric central images, 3D Layout recovery and SLAM using equirectangular panoramas, and 3D reconstruction from non-central panoramas.

关键词： computer vision omnidirectional cameras virtual environment deep learning non-central systems image generator semantic label

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Designing with AI: An Exploration of Co-Ideation with image generators 23

Designing with AI: An Exploration of Co-Ideation with Image ...

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ACM Designing Interactive Systems Conference (DIS) on Rebuilding and Resilience

作者： Chiou, Li-Yuan Liang, Rung-Huei Hung, Peng-Kai Wang, Chun-Teng Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (纸本)9781450398930

This pictorial showcases the integration of AI into the design process through the use of image generators. Following a Research Through Design (RTD) approach, we recruited 6 designers to create design proposals with AI. They were invited to collect case studies, make annotated portfolios, collaborate with the generator for design ideation, and undergo semi-structured interviews. Through this process, we gathered empirical data to investigate how designers ideate with AI and what opportunities and challenges emerge in the collaboration. The results showed that the combination of human and AI input in the design process contributes to a fresh form of self-expression and communication. AI was found to bring a distinct perspective that opens up new avenues for artistic expression. The research highlights the potential of AI to augment human creativity and the need for ongoing exploration in this field.

关键词： Midjourney design process ideation image generator

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Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023 29

Early Adoption of Generative Artificial Intelligence in Comp...

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29th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE)

作者： Smith, C. Estelle Shiekh, Kylee Cooreman, Hayden Rahman, Sharfi Zhu, Yifei Siam, Md Kamrul Ivanitskiy, Michael Ahmed, M. Ahmed Hallinan, Michael Grisak, Alexander Fierro, Gabe Colorado Sch Mines Comp Sci Golden CO 80401 USA Colorado Sch Mines Phys Golden CO USA Colorado Sch Mines Appl Math & Stat Golden CO USA Colorado Sch Mines Geophys Golden CO USA Colorado Sch Mines Chem & Biol Engn Golden CO USA Colorado Sch Mines Mech Engn Golden CO USA

ISBN: (纸本)9798400706004

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists;(2) describe computing students' GenAI-related needs and concerns for their education and careers;and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.

关键词： Generative artificial intelligence large language model code generator image generator interactive tutoring policy survey student experience AI literacy education

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Portable source initiative for distribution of cross-platform compatible multi-spectral databases

Portable source initiative for distribution of cross-platfor...

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Conference on Target and Backgrounds IX

作者： Nichols, WK USN Air Warfare Ctr Patuxent River MD 20670 USA

ISBN: (纸本)0819449342

In response to popular demand, The Visual Group has undertaken an effort known as the Portable Source Initiative, a program intended to create cross-platform compatible multi-spectral databases by building a managed source set of data and expending a minimal amount of effort republishing run-time formatted proprietary databases. By building visual and sensor databases using a variety of sources, then feeding all value-added work back into standard, open, widely used source formats, databases can be published from this "refined source data"(1) in a relatively simple, automated, and repeatable fashion. To this end, with the endorsement of the Army's PM Cargo, we have offered a sample set of the source data we are building for the CH-47F TFPS program to the visual simulation industry at large to be republished into runtime formats. The results of this effort were an overwhelming acceptance of the concepts and theories within, and support from both industry and multi-service flight simulation teams.

关键词： portable source visual database MultiSpectral database flight simulation terrain imagery image generator 3-D modeling sensor simulation refined source

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AMIGO: A TOOL FOR THE GENERATION OF SYNTHETIC HYPERSPECTRAL imageS 9

AMIGO: A TOOL FOR THE GENERATION OF SYNTHETIC HYPERSPECTRAL ...

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9th Workshop on Hyperspectral image and Signal Processing - Evolution in Remote Sensing (WHISPERS)

作者： Priego, Blanca Duro, Richard Univ A Coruna Integrated Grp Engn Res La Coruna Spain Mytech Ingn Aplicada Ltd Valencia Spain

ISBN: (纸本)9781728115818

The lack of publicly accessible hyperspectral image repositories with properly labeled and characterizedimages for the design and validation of processing algorithms, in particular, classification algorithms, is a longstanding problem in the field. In order to mitigate the need for benchmark libraries of real hyper-photographic material with reliable labeling, this work presents an tool, called Automatic Multi-dimensional image generator (AMIGO) designed and implemented to automatically create synthetic multidimensional images based on spatio-spectral parametric descriptors that can be used to simplify and facilitate the adjustment and testing of classification algorithms. This tool has been used to properly train and validate classification and denoising algorithms in many research works found in literature showing satisfactory results.

关键词： Hyperspectral images synthetic images image generator AMIGO

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An enhanced traffic simulation system for interactive traffic environment

An enhanced traffic simulation system for interactive traffi...

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IEEE Intelligent Vechicles Symposium

作者： Kuwahara, M Tanaka, S Kano, M Furukawa, M Honda, K Maruoka, K Yamamoto, T Shiraishi, T Hanabusa, H Webster, N Univ Tokyo Inst Ind Sci Tokyo Japan

ISBN: (纸本)0780389611

This study develops a microscopic traffic simulation model (KAKUMO) which is built into a mixed reality experiment system under an interactive traffic environment. We are developing a mixed reality experiment system is under development to examine several human factors such as driving behavior and travel choice behavior, which are required to design and evaluate various ITS systems. The system is characterized by the environment in which a driver, vehicles and infrastructure are dynamically interacting with each other. In this paper, we introduce the specification and performance of the micro simulation model which is built into the prototype mixed reality system.

关键词： ITS traffic simulator driving simulator image generator

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Hybrid Inception V3 and Multi-Layer Perceptron Approach with Extreme Gradient Boost Classifier for Autism Spectrum Disorder Classification 13

Hybrid Inception V3 and Multi-Layer Perceptron Approach with...

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13th International Conference on image Processing Theory Tools and Applications

作者： Bazay, Fatima Ez-zahraa El Maliani, Ahmed Drissi Mohammed V Univ Rabat Fac Sci Rabat LRIT Rabat Morocco

ISBN: (纸本)9798331541859;9798331541842

Autism Spectrum Disorders (ASD) are complex neuropsychiatric brain disorders characterized by social deficits. They are complex neuropsychiatric disorders characterized by social deficits and repetitive behaviors. Deep learning approaches have been applied to the clinical or behavioral identification of ASD. In this study, we address the classification of ASD using the Autism Brain Imaging Data Exchange I (ABIDE I) database and the Configurable Pipeline for the Analysis of Connectomes (CPAC). We integrated a single volume image generator to overcome limitations related to the small number of samples available in fMRI studies. In addition, we adopted the Inception V3 architecture and ten Multi-Layer Perceptron (MLP) architectures for feature extraction, and used Extreme Gradient Boost (XGBoost) for classification, achieving notable accuracy. In particular, hybridization of Inception V3 with different MLP architectures achieved a maximum accuracy of 96.91% at the Stanford site with a simple MLP architecture. The combination of all MLP architectures with Inception V3 also showed impressive efficiency, achieving an accuracy of up to 92.43%. These models demonstrate an exceptional ability to capture the subtle nuances of resting-state functional MRI (rs-fMRI) data, which is essential for the accurate diagnosis of autism.

关键词： image generator ABIDEI FMRI data Autism spectrum disorders Deep Neural Network Inception V3

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