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DCP++: A New Registration Method for Brain MRI 2

DCP++: A New Registration Method for Brain MRI

引用

2nd IEEE international conference on Medical Artificial Intelligence, MedAI 2024

作者： Liao, Yucheng Lei, Bohan Zhuang, Yueting Xu, Xiaoyin Zhang, Min Chu Kochen Honors College Zhejiang University Hangzhou China College of Computer Science & Technology Zhejiang University Hangzhou China College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350377613

In geometric computer vision, 3D registration is a critical task for various applications. 3D medical image registration, as one important application of 3D registration, holds substantial clinical potentials for supporting diagnosis, surgeries, and treatments through the registration of images captured at different times or using different modalities. Recently, the rapid advancement and widespread adoption of deep learning have spurred interest in applying this technology to the registration of point cloud data. In this paper, an deep-learning-based 3D brain MRI medical data registration algorithm (DCP++) is constructed. Through employing reasonable loss function, DCP++ achieves efficient and precise registration of 3D brain data. To evaluate the performance of DCP++, we apply the algorithm to the point cloud registration task of 3D human brain MRI images from the ADNI public dataset. Benchmark comparisons, ablation experiment, and visualization analyses are conducted. Experiments demonstrate that compared with state-of-the-art algorithms, DCP++ reduces the registration error by approximately 90%. Tests on real data confirm the accuracy and robustness of the DCP++ algorithm, highlighting its potential in medical practice. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Bridging the Black Box: Interpreting Transformer Attention in High-Stakes Medical and Legal AI 3

Bridging the Black Box: Interpreting Transformer Attention i...

引用

3rd international conference on Trends in Electrical, Electronics and computer Engineering, TEECCON 2024

作者： Sanjan, S. Kumar, G D Praveen Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Department of Computer Science and Engineering Mandya India

ISBN: (纸本)9798331505714

Transformer models have transformed many areas of research, showing exceptional performance in natural language processing and other areas. Their applications in high-stakes areas like healthcare and legal systems face a critical barrier: interpretability. As 'black-box' systems, these models often lack the transparency required for stakeholders to understand their decision-making processes, which is crucial in scenarios where errors can have severe consequences. We introduce a novel framework that boosts the interpretability of transformer models through advanced attention visualization techniques. It is tailored to the needs of medical and legal domains, bridging the gap between raw model outputs and actionable insights. The approach incorporates domain-specific contextual layers, letting users pinpoint and understand key factors driving the model's decisions. For example, attention maps produced by the framework can bring to the fore important regions in medical images or critical phrases in legal documents, thereby inducing trust and accountability. Overall evaluations using case studies in medical imaging with the CheXpert dataset and legal document analysis with the CaseLaw dataset illustrate the effectiveness of the proposed approach. The results show significant improvements in both performance and interpretability with high accuracy, precision, and recall achieved alongside clear visual explanations. This work advances the field of explainable AI by enabling the responsible and ethical integration of transformer models into critical applications, ensuring that these systems are not only high-performing but also transparent and trustworthy. This new framework will set a standard for the ethical and effective use of AI in high-risk domains. © 2024 IEEE.

关键词： Natural language processing systems

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Designing a graphics Processing Unit for 2D Rendering on FPGA for Educational Purpose

Designing a Graphics Processing Unit for 2D Rendering on FPG...

引用

international conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Rupak Saha Nahin Ul Sadad Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

In this paper, design and implementation of a 2D graphics processing hardware unit using FPGA for educational purpose is presented. We propose a more simple & minimalist GPU architecture, which is flexible and easy to understand than existing industrial GPUs. We present a platform for understanding GPU architecture by implementing of graphics algorithms on FPGA for students to use this in laboratories. Students can be benefited from this work as it can be used as a base understanding tool in terms of GPU architecture. It can also be extended to be used in cross disciplinary applications like 3D rendering, computer vision and signal processing visualisation. This research includes descriptions of hardware realization of 2D rasterization rendering algorithms. Hardware Description Language (HDL) called verilog is utilized in designing the architecture. Xilinx Vivado Design Suite software is used for FPGA implementation, targeting Artix 7 FPGA board, which allows users to comprehend the fundamental ideas of hardware design and their applications in graphics rendering.

关键词： visualization Three-dimensional displays Heuristic algorithms graphics processing units Signal processing algorithms computer architecture Rendering (computer graphics)

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Light Direction Reconstruction Analysis and Improvement using XAI and CG 30

Light Direction Reconstruction Analysis and Improvement usin...

引用

30th international conference in Central Europe on computer graphics, visualization and computer Vision, WSCG 2022

作者： Miller, Markus Ronczka, Stefan Nischwitz, Alfred Westermann, Rüdiger Dept. of Computer Science and Mathematics University of Applied Sciences Munich Lothstr. 64 Bavaria MunichD-80335 Germany Computer Graphics and Visualization Technical University Munich Boltzmannstr. 3/II Bavaria GarchingD-85748 Germany w&co MediaServices GmbH & Co KG Charles-de-Gaulle-Str. 8 Bavaria MunichD-81737 Germany

ISBN: (纸本)9788086943336

With rapid advances in the field of deep learning, explainable artificial intelligence (XAI) methods were introduced to gain insight into internal procedures of deep neural networks. Information gathered by XAI methods can help to identify shortcomings in network architectures and image datasets. Recent studies, however, advise to handle XAI interpretations with care, as they can be unreliable. Due to this unreliability, this study uses meta information that is produced when applying XAI to enhance the architecture - and thus the prediction performance - of a recently published regression model. This model aimed to contribute to solving the photometric registration problem in the field of augmented reality by regressing the dominant light direction in a scene. Bypassing misleading XAI interpretations, the influence of synthetic training data, generated with different rendering techniques, is furthermore evaluated empirically. In conclusion, this study demonstrates how the prediction performance of the recently published model can be increased by improving the network architecture and training dataset. © 2022 University of West Bohemia. All rights reserved.

关键词： Network architecture

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Generalist Segmentation Algorithm for Photoreceptors Analysis in Adaptive Optics imaging 27th

Generalist Segmentation Algorithm for Photoreceptors Analys...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany National Centre for Optics Vision and Eye Care Faculty of Health and Social Sciences University of South-Eastern Norway Kongsberg Norway

ISBN: (纸本)9783031781032

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labeling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labeled dataset of 20 AOSLO batches of images of 18 participants for 0∘, 1∘, and 2∘ from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0∘, 1∘, and 2∘, respectively, which is better than previously reported DL approaches. Our method minimizes the need for labeled data by only necessitating a fraction of labeled cones, which is especially beneficial in the field of ophthalmology, where labeled data can often be limited. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adaptive optics

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Financial Characteristics Graphic Representation for Borrowers Coalition in Dynamics

Financial Characteristics Graphic Representation for Borrowe...

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international conference on Management of Large-Scale System Development (MLSD)

作者： Orudzh Bayramov Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia

In this paper we present the results of numerical experiments on borrowers' coalition model designed for asset purchase. Specific digital data describes housing market. We focused on the visualization of obtained ... 详细信息

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

关键词： graphics Data visualization Numerical models Large-scale systems

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Semantic-Aware Synthesis Network for Dental Caries Image Generation from CBCT to Micro-CT

Semantic-Aware Synthesis Network for Dental Caries Image Gen...

引用

2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shan, Haoxuan Chen, Yufei Liu, Wei Qi, Shuai Zhang, Qi Tongji University School of Computer Science and Technology Shanghai200092 China Tongji University School and Hospital of Stomatology Department of Endodontics Shanghai200092 China

ISBN: (纸本)9798350386226

Dental caries is a common oral disease, and accurate imaging is vital for diagnosing, especially for assessing carious lesions and the pulp area. Cone-Beam Computed Tomography (CBCT) is a widely used imaging technology for the clinical diagnosis of caries, but its low resolution and blurred boundaries hinder clear visualization of the pulp and lesion extent. Micro Computed Tomography (Micro-CT) provides higher resolution and precision, but it's only used for ex vivo imaging. Recently, image synthesis techniques have been extensively applied to enhance the quality of medical images. However, existing synthesis methods often focus on pixel-level correspondence, neglecting the semantic information within the images. This results in limitations in reconstructing structural edges and accurately simulating anatomical regions. To address these challenges, we propose a novel model that generates Micro-CT images from CBCT images. The proposed Semantic-Aware Synthesis Network (SASN) employs a multi-task learning strategy, integrating a segmentation task to enhance the information learning capability of the shared encoder, thereby facilitating synthesis. To achieve better semantic-based synthesis, we employ a Semantic-Guided Attention Module (SGAM) to facilitate feature fusion between branches. Additionally, we introduce a semantic alignment loss to ensure semantic consistency, thereby further enhancing network performance. Experimental results demonstrate that SASN outperforms other existing methods, achieving 27.05 (± 0.18) in PSNR, 84.41[%] (± 0.97) in SSIM, and 45.47 [×1e-3] (± 1.37) in RMSE. © 2024 IEEE.

关键词： computerized tomography

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Definition and visualization of Protocols in computer Networks

Definition and Visualization of Protocols in Computer Networ...

引用

international conference on Smart Systems and Technologies (SST)

作者： Filip Škrabák Jakub Dubec Pavel Čičák FIIT STU Slovak University of Technology Bratislava Slovak Republic

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

This paper addresses the issue of network protocol design, definition, and visualization. A novel design tool for visualizing network protocols is introduced. Its implementation is described along the formalisms employed. The paper compares protocol headers as part of the analysis, highlighting unique cases found in the header fields. In addition, it discusses the formal description of protocols and compares current approaches, description languages, and their associated challenges. The implementation proposal outlines the tool’s architecture and potential for future enhancements. It also proposes a method of persistent data storage for exported protocols visualized in SVG, incorporating nested XML metadata to store both protocol definition and visualization within a single file. For this purpose, an XML schema for defining network protocols and a rendering method for SVG visualization are presented. Furthermore, the tool was implemented and released as an open-source project.

关键词： visualization Protocols Data visualization XML Metadata Smart systems Rendering (computer graphics) Design tools Proposals Standards

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A Finetuned Multimodal Deep Learning Model for Xray Image Diagnosis

A Finetuned Multimodal Deep Learning Model for Xray Image Di...

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international conference on Communications, Information System and computer Engineering (CISCE)

作者： Chen Wei Honghao Wang Zhihao Feng Huafeng Mai Zhengzhou University Zhengzhou China University of Melbourne Beijing China Shenzhen Hui Tong International School Shenzhen China University of California San Diego San diego United States

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Generative conversational AI has shown significant potential in aiding biomedical professionals, yet existing research predominantly targets single-mode textual communication. While multimodal conversational AI has advanced quickly through utilizing vast numbers of image-text pairs available online, these broad-domain vision-language models are still insufficiently refined in their ability to comprehend and discuss biomedical imagery. This article primarily addresses question-answering (QA) for medical imaging data and cancer diagnosis. We constructed a relevant instruction-based QA dataset and performed directive fine-tuning on the Qwen-VL 7B model. Thanks to the use of QLora technology, our model can be trained quickly on a Nvidia RTX 4090 graphics card and achieves better results compared to other multimodal models. The experiments showed that the Qwen- VL-7B model has best Rouge-1, Rouge-2, Rouge-L score compared to other models, meaning its highly potential for medical question-answering application.

关键词： Training Measurement visualization Biological system modeling graphics processing units X-rays Question answering (information retrieval)

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Low-cost portable system for medical image visualization

Low-cost portable system for medical image visualization

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Self Sustainable Artificial Intelligence Systems (ICSSAS), international conference on

作者： Wilver Auccahuasi Kitty Urbano Daniel Yucra-Sotomayor Ismael Junco-Quijandria Mariana Campos-Sobrino Ana Gonzales-Flores Universidad Privada del Norte Lima Perú Universidad Cientifica del Sur Lima Perú Universidad Nacional Federico Villarreal Lima Perú Universidad Autonoma de Ica Lima Perú

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Nowadays, medical images are a fundamental tool for the diagnosis and treatment of many diseases and ailments, thus requiring a considerable computational infrastructure, consisting of hardware and software; in hardwarese requires a processor of the latest generation accompanied by a graphic processing unit, which helps the visualization of the images. In relation to the necessary software, computer programs are required that can read, process and visualize the images in DICOM format, which is the standard format. In this work describe the use and exploitation of the Nvidia Tegra K1 development unit, being an embedded device with high computational capacity because it has a CPU and a GPU, which allows to work with different applications for the visualization of DICOM images, the present the configuration of the device as an integrated unit, The device can be used as a portable workstation, which would allow to perform field work in areas where there is no access to a health center, where health personnel can visit patients, which helps to diagnose and provide health services in remote populations. The device can perform multiple functionalities, depending on the program to be worked.

关键词： Performance evaluation visualization graphics processing units Software Hardware Batteries Workstations Proposals DICOM Medical diagnostic imaging

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