检索结果-内蒙古大学图书馆

Polar-Net: A Clinical-Friendly Model for Alzheimer’s Disease Detection in OCTA Images

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Liu, Shouyue Hao, Jinkui Xu, Yanwu Fu, Huazhu Guo, Xinyu Liu, Jiang Zheng, Yalin Liu, Yonghuai Zhang, Jiong Zhao, Yitian Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Cixi Biomedical Research Institute Wenzhou Medical University China School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Department of Computer Science Southern University of Science and Technology China Department of Eye and Vision Science University of Liverpool United Kingdom Department of Computer Science Edge Hill University United Kingdom

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. In this work, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model’s decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

Person re-identification by camera correlation aware feature augmentation

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Chen, Ying-Cong Zhu, Xiatian Zheng, Wei-Shi Lai, Jian-Huang School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China Department of Computer Science and Engineering Chinese University of Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Key Laboratory of Machine Intelligence and Advanced Computing Sun Yatsen University Ministry of Education China Guangdong Province Key Laboratory of Information Security China

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of

关键词： Cameras

Optimized rendering for a three-dimensional videoconferencing system

学校读者我要写书评

暂无评论

Optimized rendering for a three-dimensional videoconferencin...

IEEE International Conference on e-science and Grid computing

作者： Rachel Chu Daniel Tenedorio Jürgen P. Schulze Susumu Date Seiki Kuwabara Atsushi Nakazawa Haruo Takemura Fang-Pang Lin Department of Structural Engineering University of California San Diego La Jolla CA USA Cybermedia Center Osaka University CREST Japan Science and Technology Corporation Osaka Japan Department of Computer Science University of California San Diego La Jolla CA USA National Center of High-performance Computing Hsinchu Taiwan

Industry widely employs the two-dimensional videoconferencing system as a long distance communication tool, but current limitations such as its tendency to misrepresent eye contact prevent it from becoming more widely adopted. We are exploring the possibility of a three-dimensional videoconferencing system for future interactive streaming of point cloud data, and present the preliminary research results in this paper. We have tested thus far with one sender and one receiver, using pre-recorded data for the sender. The sender, encircled by high-definition cameras, stands and speaks in a room. A cluster of computers reconstructs each frame of the camera images into a 3D point cloud and streams it across a high-speed, low-latency network. On the receiving end, a splat-based renderer employs a new algorithm to efficiently resample the points in real-time, maintaining a user-specified frame rate. Parallel hardware projects onto multiple screens while head tracking equipment records the viewer's movements, allowing the receiver to view a stereoscopic 3D representation of the sender from multiple angles. We can combine these visuals with appropriate use of multiple audio channels to forge an unparalleled virtual experience. This next step towards immersive 3D videoconferencing brings us closer to empowering worldwide collaboration between research departments.

关键词： Teleconferencing Streaming media Cameras Communication industry Three-dimensional displays computer networks Image reconstruction Rendering (computer graphics) Clustering algorithms

New Perspectives on Recommender Systems for Industries

学校读者我要写书评

暂无评论

New Perspectives on Recommender Systems for Industries

Artificial Intelligence for Industries (AI4I)

作者： Mouzhi Ge Giovanni Pilato Fabio Persia Daniela D’Auria Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Deggendorf Germany National Research Council of Italy Institute for High Performance Computing and Networking Palermo italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila italy Faculty of Computer Science Free University of Bozen-Bolzano Bozen-Bolzano italy

Nowadays, recommender systems are increasingly being exploited in many industrial applications, including virtual museums and movie streaming platforms. In the last few years, some new perspectives provided by research paradigms such as deep learning or quantum computing, have arisen. As a result, this paper identifies four new perspectives on recommender systems: e-health, tourism, deep-learning-based, and recommender systems exploiting quantum computing. After discussing them, the paper provides the current state of the art and highlights the possible future directions for industries.

关键词： Industries Deep learning Training Quantum computing Scalability Computational modeling Virtual museums

Recommender System for Social Media: Research Challenges and Future Applications

学校读者我要写书评

暂无评论

Recommender System for Social Media: Research Challenges and...

First International Conference on Transdisciplinary AI (TransAI)

作者： Mouzhi Ge Giovanni Pilato Fabio Persia Daniela D'Auria Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Deggendorf Germany National Research Council of Italy Institute for High Performance Computing and Networking Palermo Italy Department of Information Engineering Computer Science and Mathematics University of L'Aquila L'Aquila Italy Faculty of Computer Science Free University of Bozen-Bolzano Bozen-Bolzano Italy

Social networks have evolved as a ubiquitous means of communication in our daily lives. Users consistently generate a large amount of data on social media platforms, which on the other hand has increased the complexity when selecting relevant social media content. Thus, recommender system has been emerged as a solution to provide personalized and user-centric media recommendations. However, social media has significantly broadened the scope of available inputs such as social sensing data and new types of social media. It not only offers new research opportunities for social media recommender systems, but also indicates that various domains may benefit from the application of social media recommender systems. Therefore, this paper conducts a comprehensive survey of state-of-the-art research between social media and recommender systems. The survey results identify a set of research challenges on how social media recommender systems can be applied in various domains such as healthcare and route planning.

关键词：

A workflow for seismic imaging with quantified uncertainty

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Barbosa, Carlos H.S. Kunstmann, Liliane N.O. Silva, Rômulo M. Alves, Charlan D.S. Silva, Bruno S. Filho, Djalma M.S. Mattoso, Marta Rochinha, Fernando A. Coutinho, Alvaro L.G.A. High-Performance Computing Center COPPE Federal University of Rio de Janeiro Brazil Department of Civil Engineering COPPE Federal University of Rio de Janeiro Brazil Department of Mechanical Engineering COPPE Federal University of Rio de Janeiro Brazil Department of Computer Science COPPE Federal University of Rio de Janeiro Brazil CENPES Petrobras

The interpretation of seismic images faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical properties. Understanding uncertainties' role and how they influence the outcome is an essential part of the decision-making process in the oil and gas industry. Geophysical imaging is time-consuming. When we add uncertainty quantification, it becomes both time and data-intensive. In this work, we propose a workflow for seismic imaging with quantified uncertainty. We build the workflow upon Bayesian tomography, reverse time migration, and image interpretation based on statistical information. The workflow explores an efficient hybrid parallel computational strategy to decrease the reverse time migration execution time. high levels of data compression are applied to reduce data transfer among workflow activities and data storage. We capture and analyze provenance data at runtime to improve workflow execution, monitoring, and debugging with negligible overhead. Numerical experiments on the Marmousi2 Velocity Model Benchmark demonstrate the workflow capabilities. We observe excellent weak and strong scalability, and results suggest that the use of lossy data compression does not hamper the seismic imaging uncertainty quantification. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

Structured spatial domain image and data comparison metrics 99

学校读者我要写书评

暂无评论

Structured spatial domain image and data comparison metrics

Proceedings of the conference on Visualization '99: celebrating ten years

作者： Nivedita Sahasrabudhe John E. West Raghu Machiraju Mark Janus NSF Engineering Research Center Mississippi State University DoD High Performance Computing Center Information Technology Laboratory USAE Waterways Experiment Station and NSF Engineering Research Center Mississippi State University Department of Computer Science NSF Engineering Research Center Mississippi State University Department of Aerospace Engineering NSF Engineering Research Center Mississippi State University

ISBN: (纸本)9780780358973

Often, images or datasets have to be compared, to facilitate choices of visualization and simulation parameters respectively. Common comparison techniques include side-by-side viewing and juxtaposition, in order to facilitate visual verification of verisimilitude. In this paper, we propose quantitative techniques which accentuate differences in images and datasets. The comparison is enabled through a collection of partial metrics which, essentially, measure the lack of correlation between the datasets or images being compared. That is, they attempt to expose and measure the extent of the inherent structures in the difference between images or datasets. Besides yielding numerical attributes, the metrics also produce images, which can visually highlight differences. Our metrics are simple to compute and operate in the spatial domain. We demonstrate the effectiveness of our metrics through examples for comparing images and datasets.

关键词： correlation measure rendering metrics steering

Photonic crystals in proton-exchanged LiNbO3 waveguides for photonic applications

学校读者我要写书评

暂无评论

Photonic crystals in proton-exchanged LiNbO3 waveguides for ...

IEEE PhotonicsGlobal@Singapore, IPGC

作者： Deng Jun Jia Wei Vanga Sudheer Kumar Ching Eng Png Andrew A. Bettiol Aaron J. Danner Institute of High Performance Computing Agency for Science Technology and Research (ASTAR) Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Henan Key Laboratory of Laser and O pto-electric Information Technology Zhengzhou University Henan China Department of Physics National University of Singapore Singapore

Photonic crystals realized in LiNbO 3 have a great potential to reduce the size and increase the functionality of integrated optical devices. In order to accomplish these aims, fabrication know-how must be mastered yet it remains relatively uncharted. In this work we report the fabrication and optical characterization of photonic crystal structures in annealed proton exchanged (APE) LiNbO 3 waveguides. In our work, channel waveguides were fabricated by photolithography patterning followed by immersing LiNbO 3 wafers in molten benzoic acid at 235°C for 5 h. The thickness and the composition profiles of the APE waveguides were measured using the secondary ion mass spectroscopy (SIMS) method. The experimental characterization of the photonic crystal waveguides was performed and a photonic stop band with good sharpness can be observed from the transmission spectra.

关键词： Photonics Optical fibers Optical device fabrication Optical imaging Optical polarization

Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lin, Ziqin Li, Heng Li, Zinan Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Recent advancements in pre-trained large foundation models (LFM) have yielded significant breakthroughs across various domains, including natural language processing and computer vision. These models have been particularly impactful in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM has been developed for fundus images using the Vision Transformer (VIT) and a self-supervised learning framework. This LFM has shown promising performance in fundus disease diagnosis across multiple datasets. On the other hand, deep learning models have long been challenged by dataset quality issues, such as image quality and dataset bias. To investigate the influence of data quality on LFM, we conducted explorations in two fundus diagnosis tasks using datasets of varying quality. Specifically, we explored the following questions: Is LFM more robust to image quality? Is LFM affected by dataset bias? Can fine-tuning techniques alleviate these effects? Our investigation found that LFM exhibits greater resilience to dataset quality issues, including image quality and dataset bias, compared to typical convolutional networks. Furthermore, we discovered that overall fine-tuning is an effective adapter for LFM to mitigate the impact of dataset quality issues. Copyright © 2024, The Authors. All rights reserved.

关键词： Image quality