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A Multi-staining Digital Pathology image Registration Method Based on Global and Local Computing

A Multi-staining Digital Pathology Image Registration Method...

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Yi Ding Xin Zhang Cheng Lu School of Computer and Cyber Sciences Communication University of China Beijing China Department of Radiology Guangdong Provincial People’s Hospital Guangzhou China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

To accurately evaluate the patient’s condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effectively improve the efficiency of doctors analyzing pathological images. One of the most advanced methods currently is the Virtual Alignment of Pathology image Series method, which is a multi-staining digital pathology image registration method that combines global and local calculations. However, this method may encounter certain biases when processing images with significant angle differences. Through a detailed analysis of this method, this article proposes an improvement plan which optimizes the acquisition of non-rigid registration mask images, enabling the method to obtain mask images more reasonably and achieve better registration results for images with significant angle differences. This provides more accurate judgment basis and helps doctors diagnose and develop treatment plans more accurately.

关键词： Pathology Solid modeling image registration Accuracy Medical services Software Registers

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CDCP: A Framework for Accelerating Dynamic Channel Pruning methods

CDCP: A Framework for Accelerating Dynamic Channel Pruning M...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Zhaohong Xiang Yigui Luo Yin Xie Haihong She Weiming Liang Laigang Zhao School of Software Engineering Tongji University Shanghai China Aichi Automobile (Shanghai) Company Limited

Dynamic channel pruning is a technique aimed at reducing the theoretical computational complexity and inference latency of convolutional neural networks. Dynamic channel pruning methods introduce complex additional modules for dynamically selecting channels for images. Due to the additional modules, dynamic channel pruning methods never achieve optimal acceleration effect in real world. To address this problem, we propose Consecutive Dynamic Channel Pruning (CDCP), a novel dynamic channel pruning framework unified for almost all dynamic pruning methods designed for continuous image processing. The core idea of CDCP stems from our observation that adjusting the network for all frames in semantically continuous scenes is unnecessary since adjacent frames often share similar network structures in dynamic channel pruning. CDCP introduces a simple binary classifier to determine whether the network structure needs to be adjusted for a new frame. Our method can also be used for semantically non-continuous image processing tasks with a slightly lower probability of model reuse. We validate the effectiveness of CDCP on three dynamic channel pruning methods and better acceleration effects are achieved when applied them with CDCP to the semantically continuous Waymo dataset, the nuScenes dataset, and the semantically discontinuous COCO dataset.

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image Distillation Based Screening for X-ray Crystallography Diffraction images 19

Image Distillation Based Screening for X-ray Crystallography...

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19th IEEE International Symposium on parallel and distributed processing with Applications (IEEE ISPA)

作者： Chen, Lingyao Xu, Kang Zheng, Xiaoying Zhu, Yongxin Jing, Yi Shanghai Informat Technol Res Ctr Shanghai 201210 Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China

ISBN: (纸本)9781665435741

The modern synchrotron radiation facilities are producing massive diffraction images, which present a severe problem for data processing due to the high dimensionality of imaging data. Feature recognition and selection based deep learning methods have been developed to analyze data automatically. One crucial step is to use AI to screen out the diffraction images without Bragg spots. This paper proposes a feature distillation based approach for screening. It helps to reduce over 40% raw data volume and greatly alleviates the post processing workload faced by scientists.

关键词： Deep Learning Neural Networks Bragg Spot serial crystallography

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Ancient Chinese Character image Retrieval Based on Fusing Deep Features and Skeleton Features

Ancient Chinese Character Image Retrieval Based on Fusing De...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Xinhui Wang Xuedong Tian School of Cyber Security and Computer Hebei University Baoding China Institute of Intelligent Image and Document Information Processing Hebei University Baoding China

The complex glyph structures and diverse writing styles of ancient Chinese character images lead to suboptimal performance when existing image retrieval methods are directly applied to datasets of these images. Addressing the impact on retrieval caused by the complex glyph structure and rich detail information characteristic of ancient Chinese character images, a multi-layer feature adaptive fusion model for ancient Chinese character image retrieval is designed. Firstly, a local feature extraction module is constructed to obtain low-level feature maps with different spatial receptive fields. Secondly, to better fuse features of varying scales, an improved ASFF(Adaptively Spatial Feature Fusion) method is employed to build the PSASFF(Pixel Shuffle Adaptively Spatial Feature Fusion) module for adaptive fusion of multi-layer features extracted by the designed network. Finally, in order to reduce the influence of the writing style of ancient Chinese characters on retrieval, the cosine similarity scores of image retrieval before and after fine processing are weighted and used as the ultimate similarity scores. The retrieval method proposed achieves an average retrieval accuracy of mAP@50 and mAP@30 of 0.8537 and 0.9576 respectively on the ancient Chinese character image dataset. Experimental results demonstrate the effectiveness of this method for ancient Chinese character image retrieval.

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On-Site X-ray Fluorescence Spectrometry Measurement Strategy for Assessing the Sulfonation to Improve Chemimechanical Pulping Processes

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ACS OMEGA 2022年第51期7卷 48555-48563页

作者： Rahman, Hafizur An, Siwen Norlin, Borje Persson, Erik Engstrand, Per Zeeshan, Faisal Granfeldt, Thomas Savicek, Tomig Pettersson, Gunilla Lund Univ Max IV Lab S-22591 Lund Sweden Mid Sweden Univ S-85170 Sundsvall Sweden Valmet AB S-85194 Sundsvall Sweden Czech Tech Univ Inst Expt & Appl Phys Prague 11000 Czech Republic

Minimizing the fiber property distribution would have the potential to improve the pulp properties and the process efficiency of chemimechanical pulp. To achieve this, it is essential to improve the level of knowledge of how evenly distributed the sulfonate concentration is between the individual chemimechanical pulp fibers. Due to the variation in quality between pulpwood and sawmill chips, as well as the on-chip screening method, it is difficult to develop an impregnation system that ensures the even distribution of sodium sulfite (Na2SO3) impregnation liquid. It is, therefore, crucial to measure the distribution of sulfonate groups within wood chips and fibers on a microscale. Typically, the degree of unevenness, i.e., the amount of fiber sulfonation and softening prior to defibration, is unknown on a microlevel due to excessively robust or complex processing methods. The degree of sulfonation at the fiber level can be determined by measuring the distribution of elemental sulfur and counterions of sulfonate groups, such as sodium or calcium. A miniaturized energy-dispersive X-ray fluorescence (ED-XRF) method has been developed to address this issue, enabling the analysis of sulfur distributions. It is effective enough to be applied to industrial laboratories for further development, i.e., improved image resolution and measurement time.

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Counting people in the crowd using social media images for crowd management in city events

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TRANSPORTATION 2021年第6期48卷 3085-3119页

作者： Gong, V. X. Daamen, W. Bozzon, A. Hoogendoorn, S. P. Delft Univ Technol Fac Civil Engn & Geosci Stevinweg 1 NL-2628 CN Delft Netherlands Delft Univ Technol Fac Elect Engn Math & Comp Sci Mekelweg 4 NL-2628 CD Delft Netherlands

City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.

关键词： Crowd size estimation Social media analysis image processing Face recognition Input for crowd management

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Research and Suggestions for Evaluation Metrics of On-board Video Desensitization

Research and Suggestions for Evaluation Metrics of On-board ...

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Information Science, parallel and distributed Systems (ISPDS), International Conference on

作者： Xiaolong Huang Te Hu Haojie Ji Yupeng Chen Wangdong Lu Peng He Guowei Sun Jingyan Wang State Key Laboratory of Intelligent Vehicle Safety Technology Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China China Automotive Engineering Research Institute Co. Ltd Chongqing China Zhongtong Bus Holding Co. Ltd Liaocheng China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Sensitive data leakage has become an urgent problem to be solved as more images based functionalities are being developed in vehicles. However, there is a scarcity of evaluation for on-board videos data desensitization. This research analyzes on-board video desensitization process including the image pre-processing stage, sensitive area localization stage and sensitive area desensitization stage. Considering that, this paper presents several evaluation methods and metrics of sensitive target detection performance, privacy-utility evaluation of video file metadata and image, so as to provide reference for the related research on the desensitization evaluation of on-board video.

关键词： Measurement Location awareness Information science Target recognition Current measurement distributed databases Object detection

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image denoising based on Swin Transformer Residual Conv U-Net

Image denoising based on Swin Transformer Residual Conv U-Ne...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Yong Gan Shaohui Zhou Haonan Chen Yuefeng Wang Zhengzhou University of Technology Zhengzhou China College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at $\sigma=15,33.87$ and 0.915 at $\sigma=25$, and 28.96 and 0.810 at $\sigma=50$.

关键词： Training Visualization AWGN Computational modeling Noise reduction Transformers Data augmentation

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A Multimodal Fusion Framework for Fake News Detection via Multi-Attention Mechanism

A Multimodal Fusion Framework for Fake News Detection via Mu...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Yongxin Yu Yanqiang Li Ke Ji Zhenxiang Chen Kun Ma Xiaofan Zhao School of Information Science and Engineering University of Jinan Jinan China School of Information and Cyber Security People’s Public Security University of China Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Social media platforms have emerged as the primary channels for the general public to access and share information. However, the rapid dissemination of news has led to the emergence of a significant amount of unverified content, posing a serious threat to media credibility and network security. The current solutions primarily focus on detecting fake news by extracting and fusing features from images and text, but they have not fully utilized the relevance within and between modalities, resulting in suboptimal fusion effects. In this paper, we propose a multimodal fusion framework via multi-attention mechanism (MFMA), which considers not only the semantic and relevance features of images, textual content, and Optical Character Recognition (OCR) text from the news, but also the multimodal relevance features between them. First, we extract text within images (OCR text) to address the issue of inadequate utilization of the visual modality in traditional methods. Second, we select more robust feature extractors to capture the independent characteristics of each modality and use a pre-trained FcaNet model to extract more comprehensive image quality features. Additionally, two relevance extraction modules are designed to achieve feature fusion within and between modalities. Finally, the combined features are fed into a classifier to determine the authenticity of the news. The experimental results and analysis indicate that the model we proposed effectively enhances the performance of fake news detection.

关键词： image quality Visualization distributed processing Social networking (online) Optical character recognition Semantics Network security Feature extraction Data mining Fake news

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Virtual prototyping of complex optical systems on multiprocessor workstations

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OPTICAL ENGINEERING 2023年第2期62卷 021006-021006页

作者： Zhdanov, Andrey Zhdanov, Dmitry Sorokin, Maksim Potemin, Igor ITMO Univ St Petersburg Russia

Nowadays, optical designers often use multiprocessor workstations for the virtual prototyping of complex optical systems. Modern workstations may have several CPUs with up to 128 virtual cores each and a non-uniform access speed to different memory areas. Effective implementation of virtual prototyping methods and algorithms requires the development of special methods for efficient algorithm parallelization and shared memory access. As a basis for the virtual prototyping, we proposed a progressive backward photon mapping method that allows for reducing the amount of data used by photon maps, speeding up the luminance calculation process, and estimating the luminance errors for the resulting image. The main algorithmic complexity of this method is the need to synchronize data when calculating and accumulating the luminance of indirect and caustic illumination. The authors propose the three-level semi-synchronous parallelization method, which consists of fully synchronous, semi-synchronous, and asynchronous levels with illumination processing effectively distributed among the computation threads. The main benefit of the developed method is that it does not require additional synchronization when accumulating luminance, thus increasing the image synthesis speed. The designed three-level method can also be used in distributed systems with good scalability. The results obtained with virtual prototypes of complex optical systems are presented.(c) The Authors.

关键词： virtual prototyping ray tracing parallel computing realistic rendering backward photon mapping multiprocessor workstations

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