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Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases

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arXiv 2024年

作者： Chen, Yifei Zhang, Chenyan Chen, Ben Huang, Yiyu Sun, Yifei Wang, Changmiao Fu, Xianjun Dai, Yuxing Qin, Feiwei Peng, Yong Gao, Yu HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen518172 China School of Artificial Intelligence Zhejiang College of Security Technology Wenzhou325016 China National-local Joint Engineering Research Center for Digitalized Electrical Design Technology Wenzhou University Wenzhou325035 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Department of Hematology Zhejiang Hospital Hangzhou310018 China

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients’ blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis. Contemporary leukocyte detection methods exhibit limitations in dealing with images with fewer leukocyte features and the disparity in scale among different leukocytes, leading to unsatisfactory results in most instances. To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte scale disparity, we designed the High-level Screening-feature Fusion Pyramid (HS-FPN), enabling multi-level fusion. This model uses high-level features as weights to filter low-level feature information via a channel attention module and then merges the screened information with the high-level features, thus enhancing the model’s feature expression capability. Further, we address the issue of leukocyte feature scarcity by incorporating a multi-scale deformable self-attention module in the encoder and using the self-attention and cross-deformable attention mechanisms in the decoder, which aids in the extraction of the global features of the leukocyte feature maps. The effectiveness, superiority, and generalizability of the proposed MFDS-DETR method are confirmed through comparisons with other cutting-edge leukocyte detection models using the private WBCDD, public LISC and BCCD datasets. Our source code and private WBCCD

关键词： Object detection

Dual-Level Decoupled Transformer for Video Captioning

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arXiv 2022年

作者： Gao, Yiqi Hou, Xinglin Suo, Wei Sun, Mengyang Ge, Tiezheng Jiang, Yuning Wang, Peng School of Computer Science Northwestern Polytechnical University China National Engineering Lab for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Alibaba Group China

Video captioning aims to understand the spatio-temporal semantic concept of the video and generate descriptive sentences. The defacto approach to this task dictates a text generator to learn from offline-extracted motion or appearance features from pre-trained vision models. However, these methods may suffer from the so-called "couple" drawbacks on both video spatio-temporal representation and sentence generation. For the former, "couple" means learning spatio-temporal representation in a single model(3DCNN), resulting the problems named disconnection in task/pre-train domain and hard for end-to-end training. As for the latter, "couple" means treating the generation of visual semantic and syntax-related words equally. To this end, we present D2 - a dual-level decoupled transformer pipeline to solve the above drawbacks: (i) for video spatio-temporal representation, we decouple the process of it into "first-spatial-then-temporal" paradigm, releasing the potential of using dedicated model(e.g. image-text pre-training) to connect the pre-training and downstream tasks, and makes the entire model end-to-end trainable. (ii) for sentence generation, we propose Syntax-Aware Decoder to dynamically measure the contribution of visual semantic and syntax-related words. Extensive experiments on three widely-used benchmarks (MSVD, MSR-VTT and VATEX) have shown great potential of the proposed D2 and surpassed the previous methods by a large margin in the task of video captioning. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

LF-YOLOv7: Improved YOLOv7 Based on Lightweight Modules and Novel Feature Fusion for Object Detection on Drone-Captured Scenarios

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LF-YOLOv7: Improved YOLOv7 Based on Lightweight Modules and ...

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Wangyu Jiang Le Wang Guojun Mao Meng Sun Fayaz Ali Dharejo Ghulam Ali Mallah Fujian Provincial Key Lab of Big Data Mining&Applications Fujian University of Technology Fuzhou Fujian Province China Department of Electrical Engineering and Computer Science Khalifa Univeristy AbuDhabi UAE Department of Computer Science Shah Abdul Latif University Khairpur Pakistan

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

To address challenges in drone-captured images, including small-sized objects, multiple scales, and object diversity, we propose a novel lightweight and feature fusion based object detection model, called LF-YOLOv7. Firstly, a comprehensive high-resolution refinement design is investigated through simultaneously restructuring the head and optimizing the backbone, achieving more accurate assignment of multiple anchor boxes of different sizes and higher spatial resolution with lightweight mode. Furthermore, we present a new feature fusion module SM-BiFPN, which effectively integrates shallow details through cross-layer connections and enables the model to obtain more feature information related to small objects. Experimental evaluation shows that our method achieves a more competitive results than the benchmark and other compared state-of-the-art models in terms of mAP50 and the number of parameters.

关键词： Cross layer design Head Accuracy Scientific computing Computational modeling Object detection Benchmark testing

Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling

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arXiv 2023年

作者： Wang, Guiqin Zhao, Peng Zhao, Cong Yang, Shusen Cheng, Jie Leng, Luziwei Liao, Jianxing Guo, Qinghai School of Computer Science and Technology Xi’an Jiaotong University China ACS Lab Huawei Technologies China School of Mathematics and Statistics Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University China

Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple instance learning(MIL), where the predictions of unlabeled instances are supervised by classifying labeled bags. The MIL-based methods are relatively well studied with cogent performance achieved on classification but not on localization. Generally, they locate temporal regions by the video-level classification but overlook the temporal variations of feature semantics. To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics. Specifically, our model entails two components, the first is an unsupervised change-points detection module that detects change-points by learning the latent representations of video features in a temporal hierarchy based on their rates of change, and the second is an attention-based classification model that selects the change-points of the foreground as the boundaries. To evaluate the effectiveness of our model, we conduct extensive experiments on two benchmark datasets, THUMOS-14 and ActivityNetv1.3. The experiments show that our method outperforms current state-of-the-art methods, and even achieves comparable performance with fully-supervised methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

A Routing Optimization in Wireless Sensor Networks Based on Reverse Elite Sparrow Search Algorithm

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Journal of Network Intelligence

Journal of Network Intelligence 2021年第4期6卷 763-775页

作者： Ning, Chun-Yuan Liao, Lyu-Chao Nguyen, Trong-The Huang, Kuan-Chun Pan, Jeng-Shyang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Multimedia Communications Lab University of Information Technology VNU-HCM Viet Nam Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

This study suggests a solution to the routing optimization insufficiency based on the reverse elite sparrow search algorithm (RESSA) for energy balancing and network lifetime prolonging in wireless sensor networks (WSN). The node’s consumption and the remaining energy priority to balance consumption in the data forwarding to the cluster head are considered the optimal path evaluation function. The obtained results of the experimental the suggested scheme are compared with the other methods show that the proposed scheme increases data forwarding round numbers and consumes less energy, the remaining energy more, and higher efficiency of node energy consumption. © The Authors.

关键词： Energy utilization

A Method of Realizing External Network Access to Intranet for Embedded Web Server 2

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A Method of Realizing External Network Access to Intranet fo...

2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Zhang, Xian Qu, Taiguo Tang, Pengju Liu, Yiwen Key Lab. of Intelligent Contr. Technol. for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China School of Computer Science and Engineering Huaihua University Huaihua China

ISBN: (纸本)9781665464017

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects. © 2022 IEEE.

关键词： Intranets

Identifying discreditable firms in a large-scale ownership network

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arXiv 2022年

作者： Zhou, Tao Lee, Yan-Li Li, Qian Chen, Duanbing Xie, Wenbo Wu, Tong Zeng, Tu CompleX Lab University of Electronic Science and Technology of China Chengdu711731 China School of Computer and Software Engineering Xihua University Chengdu610039 China Business Big Data Inc. Chengdu610095 China Union Big Data Inc. Chengdu610299 China School of Computer Science Southwest Petroleum University Chengdu610500 China

Violations of laws and regulations about food safety, production safety, quality standard and environmental protection, or negative consequences from loan, guarantee and pledge contracts, may result in operating and credit risks of firms. The above illegal or trust-breaking activities are collectively called discreditable activities, and firms with discreditable activities are named as discreditable firms. Identification of discreditable firms is of great significance for investment attraction, bank lending, equity investment, supplier selection, job seeking, and so on. In this paper, we collect registration records of about 1.13 × 108 Chinese firms and construct an ownership network with about 6 million nodes, where each node is a firm who has invested at least one firm or has been invested by at least one firm. Analysis of publicly available records of discreditable activities show strong network effect, namely the probability of a firm to be discreditable is remarkably higher than the average probability given the fact that one of its investors or investees is discreditable. In comparison, for the risk of being a discreditable firm, an investee has higher impact than an investor in average. The impact of a firm on surrounding firms decays along with the increasing topological distance, analogous to the well-known "three degrees of separation" phenomenon. The uncovered correlation of discreditable activities can be considered as a representative example of network effect, in addition to the propagation of diseases, opinions and human behaviors. Lastly, we show that the utilization of the network effect largely improves the accuracy of the algorithm to identify discreditable firms. Copyright © 2022, The Authors. All rights reserved.

关键词： Crime

A nearly optimal distributed algorithm for computing the weighted girth

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science China(Information sciences) 2021年第11期64卷 80-94页

作者： Qiang-Sheng HUA Lixiang QIAN Dongxiao YU Xuanhua SHI Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology School of Computer Science and Technology Shandong University

Computing the weighted girth, which is the sum of weights of edges in the minimum weight cycle,is an important problem in network analysis. The problem for distributively computing girth in unweighted graphs has garnered lots of attention, but there are few studies in weighted graphs. In this paper, we propose a distributed randomized algorithm for computing the weighted girth in weighted graphs with integral edge weights in the range [1, nc], where n is the number of vertices and c is a constant. The algorithm is devised under the standard synchronous CON GE S T model, which limits each vertex can only transfer O（log n） bits information along each incident edge in a round. The upper bound of the algorithm is O（n log2n） rounds. We also prove the lower bound for computing the weighted girth is ?（D + n/log n） where D is the hop diameter of the weighted graph. This means our distributed algorithm is optimal within a factor of O（log3n）.

关键词： distributed algorithms weighted girth ${\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}$ model communication complexity round complexity

MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit dataflows

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MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

IEEE Symposium on High-Performance Computer Architecture

作者： Wenju Zhao Pengcheng Yao Dan Chen Long Zheng Xiaofei Liao Qinggang Wang Shaobo Ma Yu Li Haifeng Liu Wenjing Xiao Yufei Sun Bing Zhu Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore School of Computer Electronics and Information Guangxi University NanNing China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of $1,032.23 \times$ and $10.51 \times$, and energy efficiencies of $1,169.03 \times$ and $9.96 \times$, respectively.

关键词： Computational modeling Neural networks Redundancy Pipelines Memory management Parallel processing Vectors Energy efficiency Hardware acceleration