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检索条件"任意字段=International Conference on Computer Graphics, Imaging and Visualization"

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Accurate Diagnosis by Magnetic Resonance imaging Using Deep Learning

Accurate Diagnosis by Magnetic Resonance Imaging Using Deep ...

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Optimization Computing and Wireless Communication (ICOCWC), international conference on

作者： A Rengarajan Zahid Ahmed Rajendra P. Pandey Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India

deep mastering has revolutionized the field of scientific imaging, providing practical affected person prognosis competencies. A current example is deep studying for Magnetic Resonance imaging (MRI) acquisition, reconstruction, segmentation, and interpretation. Deep mastering-primarily based strategies leverage convolutional neural networks (CNNs) to automatically classify and section not unusual disease areas in MRI scans, allowing particular and correct prognosis. Those computerized strategies can potentially conquer the bottleneck of the complex work-intensive guide segmentation and visualization procedure. Moreover, they can provide an extra complete assessment of complex sicknesses. Through incorporating both the semantic and spatial information of image information, the overall performance of deep-gaining knowledge of-based totally structures for MRI evaluation has dramatically progressed. Moreover, CNN-based total MRI segmentation has proven promise in targeted and effective remedies for numerous diseases, including mind tumors, stroke, and dementia. The demonstration of superior effects in segmentation and classification tasks, in terms of accuracy and efficiency, shows the potential of deep learning-based techniques as an effective device within the automation of MRI analysis.

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Visual Tomography: Physically Faithful Volumetric Models of Partially Translucent Objects

Visual Tomography: Physically Faithful Volumetric Models of ...

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international conference on 3D imaging, Modeling, Processing, visualization and Transmission (3DIMPVT)

作者： David Nakath Xiangyu Weng Mengkun She Kevin Köser Marine Data Science University of Kiel Christian-Albrechts-Platz 4 Kiel Germany Oceanic Machine Vision GEOMAR – Helmholtz Centre for Ocean Research Kiel Wischhofstr. 1-3 Kiel Germany

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

When created faithfully from real-world data, Digital 3D representations of objects can be useful for human or computer-assisted analysis. Such models can also serve for generating training data for machine learning approaches in settings where data is difficult to obtain or where too few training data exists, e.g. by providing novel views or images in varying conditions. While the vast amount of visual 3D reconstruction approaches focus on non-physical models, textured object surfaces or shapes, in this contribution we propose a volumetric reconstruction approach that obtains a physical model including the interior of partially translucent objects such as plankton or insects. Our technique photographs the object under different poses in front of a bright white light source and computes absorption and scattering per voxel. It can be interpreted as visual tomography that we solve by inverse raytracing. We additionally suggest a method to convert non-physical NeRF media into a physically-based volumetric grid for initialization and illustrate the usefulness of the approach using two real-world plankton validation sets, the lab-scanned models being finally also relighted and virtually submerged in a scenario with augmented medium and illumination conditions. Please visit the project homepage at ***. ***/go/vito

关键词： Solid modeling Three-dimensional displays Computational modeling Plankton Training data Tomography Rendering (computer graphics)

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Lumbar Spine MRI Images Quality Enhancement through Contrast Limited Adaptive Histogram Equalization

Lumbar Spine MRI Images Quality Enhancement through Contrast...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Mahender Erukala Suresh Kumar Sanampudi Computer Science and Engineering JNTUH Hyderabad Telangana India Department of Information Technology JNTUH College of Engineering Jagtial Telangana India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

Lumbar spine MRI is important for accurately diagnosing spinal issues, where the clarity and quality of images highly influence medical examinations. This work determines the effectiveness of Contrast Limited Adaptive Histogram Equalization (CLAHE) as a contrast enhancement preprocessing approach to enhance lumbar spine MRI images visualization. Considering the importance of clear and precise visualization in clinical settings, this approach is evaluated in comparison to the conventional Histogram Equalization (HE) and the Retinex Algorithm, both widely used for image enhancement. The comparison study focuses on performance metric to evaluate the quality of images, including PSNR, SSIM, EPI and CPU time. Results illustrate that CLAHE outperforms HE and the Retinex , delivering better PSNR and SSIM values, leading to greater contrast enhancement and structural feature retention as well. These findings suggest that CLAHE provides more successful preprocessing strategy for lumbar spine MRI, having great potential to increase diagnostic accuracy. Further research focuses on the integration of CLAHE with optimization techniques to improve image enhancement in medical imaging.

关键词： Image quality Histograms visualization Accuracy Magnetic resonance imaging Spine Adaptive equalizers Time measurement Image enhancement Optimization

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A Comparative Analysis of XAI Techniques for Medical imaging: Challenges and Opportunities

A Comparative Analysis of XAI Techniques for Medical Imaging...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Paola Barra Attilio Della Greca Ilaria Amaro Augusto Tortora Mariacarla Staffa Department of Science and Technology University of Naples Parthenope Naples Italy Department of Computer Science University of Salerno Salerno Italy Department of Physics "E. Pancini" University of Naples Federico II Naples Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The application of artificial intelligence (AI) in medical imaging has significantly improved diagnostic accuracy. However, the reliance on black-box models remains a barrier to its widespread adoption in clinical settings. This article compares the main techniques of explainable AI (XAI), such as Grad-CAM, LIME, and SHAP, evaluating their effectiveness in interpreting deep learning models used in medical imaging. Two case studies are analyzed, comparing the three methods and highlighting their strengths and weaknesses. The results of this analysis show that Grad-CAM provides intuitive visualizations; LIME offers excellent flexibility in application; and SHAP delivers complete and accurate explanations, despite its high computational load.

关键词： Heating systems visualization Accuracy Explainable AI Computational modeling Stochastic processes Fasteners Real-time systems Medical diagnostic imaging Load modeling

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Digital imaging Analyzing and Reproducing Color, Texture, and Material Appearance

Digital Imaging Analyzing and Reproducing Color, Texture, an...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Sujin Jose Arul Sajja Suneel Manish Gupta K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Automobile Engineering Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Digital photography has made great strides in showing colour and texture along with specific material details. Standard approaches usually do not attain realistic representations. The Multifaceted Realism Synthesis presented in this document resolves these problems and outshines classic strategies in accuracy and realism. The MRS framework incorporates three core algorithms: With this approach we can obtain more consistent and accurate colour reproduction using the Colour Harmonisation Algorithm. The Physically Informed Material Rendering and Typical Texture Synthesis (ATS) generate more natural textures that adapt to local details rather than using median filtering or morphological methods. The MRS approach adjusts materials appearance texture and colour to deliver superior realism adaptability and precision beating current methods. This new approach can change digital photography across different areas such as entertainment and healthcare. The technique of MRS has made a substantial improvement in the quest for hyper-real visuals in digital photography.

关键词： Measurement visualization Accuracy Image color analysis MIMICs Transforms Medical services Rendering (computer graphics) Digital photography Standards

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A Deep Learning-Based Bengali Visual Commonsense Reasoning System

A Deep Learning-Based Bengali Visual Commonsense Reasoning S...

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computer graphics and Image Processing (CGIP), international conference on

作者： Md Yousuf Zaman Md Tareq Zaman Sounava Pandit Jeba Sultana Faisal Muhammad Shah Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350374186

ISBN: (纸本)9798350374193

Visual commonsense reasoning, an integral aspect of human intelligence, extends beyond mere object identification, encompassing the nuanced inference of actions, intentions, and emotions from visual content. While humans effortlessly excel in this cognitive domain, contemporary Bengali vision systems encounter obstacles in attaining comparable proficiency. This is primarily attributed to the need for advanced cognitive faculties and common-sense reasoning abilities. This research paper introduces the Bengali Visual Commonsense Reasoning System (BVCRS), differentiating from prevailing Bengali vision models that predominantly focus on tasks such as image captioning, object recognition, segmentation, and visual question answering. The Bengali Visual Commonsense Reasoning(BVCR) model, coupled with our Bangla-VCR dataset, stands as a pivotal asset for advancing research in Bengali visual understanding and reasoning. Our BVCR model aspires to capture real-world dynamics and human intentions in Bengali by incor-porating state-of-the-art deep learning *** leverage a pre-trained ResNet50 for visual features and fine-tuned M-BERT for Bengali *** extract relationships between textual and visual features for deducing answers and reasoning, a bidirectional LSTM with attention mechanism is employed. In experiments, BVCR achieved 42.6% accuracy in visual question answering and 44.2% in visual commonsense reasoning. Our work highlights the challenges in attaining human-level performance in Bengali visual reasoning, providing insights for future research.

关键词： Deep learning visualization Analytical models Human intelligence Computational modeling Feature extraction Question answering (information retrieval)

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Exploring The Efficacy of Multi-modal LLM In Endometrial Cancer Diagnosis

Exploring The Efficacy of Multi-modal LLM In Endometrial Can...

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Innovation and Novelty in Engineering and Technology (INNOVA), international conference on

作者： B. S Sumitha Mohammed Tajuddin Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India Department of CSE (Cyber Security) Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Endometrial Carcinoma (EC) remains a significant diagnostic challenge due to its heterogeneous histopathology and clinical presentation. Recent advances in Visual Language Models (VLMs) have shown promise in medical imaging. One of the emergent technologies involves to explore large language models (LLMs) abilities with multimodal data. In this research, we find how Integration of VLMs in medical imaging analysis offers substantial benefits, includes better diagnostic accuracy, enhanced clinical decision support. This study demonstrates the efficacy of a multi-modal VLM framework for automated detection and classification of Endometrial Cancer stages, achieving 92% accuracy. Our approach integrates Magnetic resonance image (MRI) of endometrial Cancer and clinical text data to enhance diagnostic decision-making.

关键词： visualization Technological innovation Accuracy Histopathology Magnetic resonance imaging Large language models Decision making Magnetic resonance Cancer Biomedical imaging

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A Review of Evolutionary Optimization Methods for Information visualization and Feature Space Exploration

A Review of Evolutionary Optimization Methods for Informatio...

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IEEE international Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Marius Gavrilescu Florin Leon Lavinia-Eugenia Ferariu Cristian-Nicolae Buțincu Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Romania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Evolutionary optimization plays an important role in the representation of information from data sets originating from various technical fields and natural sciences, as it helps to explore parameter spaces for meaningful representations. Quality-Diversity (QD) methods, notably MAP-Elites variants, prove effective in diverse fields, emphasizing their ability to provide sets of high-performing solutions. This review discusses challenges in single- and multi-objective optimization, with applications in multiple directions such as image processing, visualization, and medical imaging. It also reviews QD algorithms and highlights advancements in algorithmic adaptations, user-driven optimization and the potential to explore complex feature spaces. The presented works contribute to understanding and applying evolutionary optimization for solving visualization and feature space exploration problems in various domains.

关键词： visualization Technological innovation Uncertainty Costs Reviews Image processing Optimization methods

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Continuous Bernoulli Distribution for More Realistic Ultrasound Reconstruction with NeRF

Continuous Bernoulli Distribution for More Realistic Ultraso...

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IEEE international Symposium on Applications of Ferroelectrics (ISAF)

作者： Yimeng Dou Tomy Varghese Electrical and Computer Engineering Medical Physics University of Wisconsin-Madison Madison WI USA Medical Physics Electrical and Computer Engineering University of Wisconsin-Madison Madison WI USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Three-dimensional visualization offers a significantly improved depiction of the underlying anatomical structure; however conventional ultrasound typically provides two-dimensional B-mode images of anatomical regions in real time. Recent breakthroughs in computer graphics have leveraged the ability of Neural Radiance Fields (NeRF) to reconstruct three-dimensional scenes implicitly. This is achieved by formulating the three-dimensional scenes as continuous signals using multi-layer perception (MLP). The reconstructed images can be viewed from novel perspectives using a differentiable rendering method. However, current methods cannot recover complex ultrasound anatomical structures and can introduce artifacts, which impact the reconstruction quality. We propose a probabilistic and differentiable approach to estimate backscattered data to provide a more realistic three-dimensional ultrasound reconstruction with NeRF. Our proposed method achieves an improved peak signal-to-noise ratio (PSNR) score of 27.73±0.98 dB compared to the baseline method with 26.31±1.91 dB, along with an improved structural similarity index measure (SSIM) value of 0.47±0.21 to 0.54±0.19 for NeRF.

关键词： Ultrasonic imaging PSNR Ultrasonic variables measurement Anatomical structure Neural radiance field Rendering (computer graphics) Probabilistic logic Real-time systems Indexes Image reconstruction

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Visual Object Tracking Based on Double Siamese Networks

Visual Object Tracking Based on Double Siamese Networks

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Big Data, Information and computer Network (BDICN), international conference on

作者： Ruiyu Yang Yanjie Wang Wenbing Cai Engineering Research Center of Wideband Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Beijing Institute of Tracking and Telecommunications Technology Beijing China

This paper deeply decouples two different tasks in the conventional Siamese-based trackers structure, so that the model can better optimize the two different tasks (classification and regression). Specifically, two heterogeneous features are extracted by building two independent feature extractors. The interaction between multiple feature maps is realized through attention mechanism, which can establish dependency relationships between multi-branches. In addition, a novel multichannel interaction method is proposed. The tracking results on multiple datasets show that the proposed tracking algorithm greatly improves the tracking accuracy of the tracker and can run at 60 FPS on GPU.

关键词： visualization Buildings graphics processing units computer architecture Big Data Feature extraction computer networks

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