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Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

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Intelligent Medicine 2022年第2期2卷 82-87页

作者： Jiaqi Lu Ruiqing Liu Yuejuan Zhang Xianxiang Zhang Longbo Zheng Chao Zhang Kaiming Zhang Shuai Li Yun Lu Department of Gastrointestinal Surgery Affiliated Hospital of Qingdao UniversityQingdaoShandong 266055China Department of Pathology Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China State Key Laboratory of Virtual Reality Technology and System Beihang UniversityBeijing 100191China Shandong Provincial Key Laboratory of Digital Medicine and Computer Assisted Surgery QingdaoShandong 266003China Research Institute of Digital Medicine and Computer Aided Surgery Qingdao UniversityQingdaoShandong 266000China

Objective Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical ***,there are significant differences and difficulties associated with manually identifying tumor *** study used the Faster region convolutional neural network(RCNN)model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal *** We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October *** pathologists used LabelImg software to identify tumor buds and to count their ***,1,000 images were screened,and the total number of tumor buds was approximately 3,226;the images were randomly divided into a training set and a test set at a ratio of 6:*** the images in the training set were manually identified,the identified buds in the 600 images were used to train the Faster RCNN identification *** the establishment of the artificial intelligence identification detection platform,400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumor ***,by comparing the results of the Faster RCNN system and the identification information of pathologists,the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate *** selected performance indicators include accuracy,precision,specificity,***(receiver operator characteristic)and AUC(area under the curve)were used to quantify the performance of the system to automatically identify tumor budding areas and *** The AUC of the receiver operating charac

关键词： Artificial intelligence Tumor budding Colorectal cancer Pathological section

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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PGMAN: An unsupervised generative multi-adversarial network for pan-sharpening

arXiv

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arXiv 2020年

作者： Zhou, Huanyu Liu, Qingjie Wang, Yunhong The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Hangzhou Innovation Institute Beihang University

Pan-sharpening aims at fusing a low-resolution (LR) multi-spectral (MS) image and a high-resolution (HR) panchromatic (PAN) image acquired by a satellite to generate an HR MS image. Many deep learning based methods have been developed in the past few years. However, since there are no intended HR MS images as references for learning, almost all of the existing methods down-sample the MS and PAN images and regard the original MS images as targets to form a supervised setting for training. These methods may perform well on the down-scaled images, however, they generalize poorly to the full-resolution images. To conquer this problem, we design an unsupervised framework that is able to learn directly from the full-resolution images without any preprocessing. The model is built based on a novel generative multi-adversarial network. We use a two-stream generator to extract the modality-specific features from the PAN and MS images, respectively, and develop a dual-discriminator to preserve the spectral and spatial information of the inputs when performing fusion. Furthermore, a novel loss function is introduced to facilitate training under the unsupervised setting. Experiments and comparisons with other state-of-the-art methods on GaoFen-2, QuickBird and WorldView-3 images demonstrate that the proposed method can obtain much better fusion results on the full-resolution images. Code is available1 Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Disentangling Local and Global Information for Light Field Depth Estimation

Disentangling Local and Global Information for Light Field D...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xueting Yang Junli Deng Rongshan Chen Ruixuan Cong Wei Ke Hao Sheng School of Information and Communication Engineering Communication University of China Beijing P.R. China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing P.R. China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou P.R. China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R. China

Accurate depth estimation from light field images is essential for various applications. Deep learning-based techniques have shown great potential in addressing this problem while still face challenges such as sensitivity to occlusions and difficulties in handling untextured areas. To overcome these limitations, we propose a novel approach that utilizes both local and global features in the cost volume for depth estimation. Specifically, our hybrid cost volume network consists of two complementary sub-modules: a 2D ContextNet for global context information and a matching cost volume for local feature information. We also introduce an occlusion-aware loss that accounts for occlusion areas to improve depth estimation quality. We demonstrate the effectiveness of our approach on the UrbanLF and HCInew datasets, showing significant improvements over existing methods, especially in occluded and untextured regions. Our method disentangles local feature and global semantic information explicitly, reducing the occlusion and untextured area reconstruction error and improving the accuracy of depth estimation.

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A Novel Visual Perception Framework for Unmanned Aerial Vehicles: Challenges and Approaches

A Novel Visual Perception Framework for Unmanned Aerial Vehi...

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Chinese Automation Congress (CAC)

作者： Xuan Li Haibin Duan Hong Mo Fei-Yue Wang Peng Cheng Laboratory Shenzhen China The State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical Engineering Beihang University Beijing China The School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426480

Change detection (CD) is an important preprocessing process in visual perception. Due to the difficulties of collection and labeling images in water scnens, the design and validation of the change detection algorithm is more difficult than expected. In this paper, we present a novel visual perception framework based on parallel vision for unmanned aerial vehicle (UAV). The visual perception framework consists of artificial scenes, computational experiments and parallel execution. In artificial scenes, we construct a water scene and automatically generated pixel-level image annotation. Next, computational experiments are carried out on different domain dataset. The experiment results show that: 1) mixed images effectively improve the performance of change detection model; 2) domain shift problems still greatly affect the algorithm performance.

关键词： Automation Change detection algorithms Computational modeling Image annotation Autonomous aerial vehicles Labeling Detection algorithms

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Correction: Efficacy of a virtual 3D Simulation-Based Digital Training Module for Building Dental technology Students' Long-Term Competency in Removable Partial Denture Design: Prospective Cohort Study

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JMIR serious games 2024年 12卷 e63113页

作者： KeXin Liu YaQian Xu ChaoYi Ma Na Yu FaBing Tan Yi Li YaXin Bai XiaoMing Fu JiaWu Wan DongQi Fan HuBin Yin MeiXi Chen HongJi Chen Lin Jiang JinLin Song Ping Ji XiaoHan Zhao MengWei Pang College of Stomatology Chongqing Medical University Chongqing China. Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences Chongqing China. Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education Chongqing China. Beijing Unidraw Virtual Reality Technology Research Institute Co Ltd Beijing China. State Key Laboratory of Virtual Reality Technology and Systems BeiHang University Beijing China.

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Lower Limb Balance Rehabilitation of Post-stroke Patients Using an Evaluating and Training Combined Augmented reality System

Lower Limb Balance Rehabilitation of Post-stroke Patients Us...

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IEEE International Symposium on Mixed and Augmented reality Workshops (ISMARW)

作者： Shuwei Chen Ben Hu Yang Gao Zhiping Liao Jianhua Li Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Ningbo University State Key Laboratory of Virtual Reality Technology and Systems Research Institute of Frontier Science Beihang University Sir Run Run Shaw Hospital Zhejiang University School of Medicine

ISBN: (数字)9781728176758

ISBN: (纸本)9781728176765

Augmented/virtual reality applications can provide immersive and interactive virtual environment for motor rehabilitation using the collaborative stimulations of multiple sensory channels such as sight, hearing, and movement, enhance the rehabilitation effect through repetitions, feedbacks, and encouragement. In this paper, we propose an evaluating and training integrated application for the rehabilitation of patients with lower limb balance disorder. The AR-based evaluation module visualizes the limits of lower limbs patients' balance abilities and provides quantitative data to their therapists, then rehabilitation therapists can customize personalized VR training games accordingly.

关键词： Training Human computer interaction virtual environments Medical treatment Games Usability Augmented reality

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Nonsingular field-of-view constrained cooperative guidance with improved time-to-go estimation

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Chinese Journal of Aeronautics 2025年

作者： Xiaofei YANG Yunjie WU Weibo XIA Xiaodong LIU Haibin DUAN School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China Beĳing Aerospace Automatic Control Institute Beĳing 100854 China

This paper proposes a distributed nonsingular cooperative guidance law for multiple flight vehicles with Field-of-View (FOV) constraints. First, a novel time-to-go estimation is developed based on a FOV-constrained Proportional Navigation Guidance (FPNG) law. Then, the FPNG law is augmented with a cooperative guidance term to achieve consensus of time-to-go with predefined-time convergence prior to the impact time. A continuous auxiliary function is introduced in the bias term to avoid the singularity of guidance command. Moreover, the proposed guidance law is extended to the three-dimensional guidance scenarios and the moving target with the help of a predicted interception point. Finally, several numerical simulations are conducted, and the results verify the effectiveness, robustness, and advantages of the proposed cooperative guidance law.

关键词： Flight vehicles Cooperative guidance Field-of-view constraints Time-to-go estimation Predefined-time convergence

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Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference

arXiv

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arXiv 2021年

作者： Niu, Guanglin Li, Bo Zhang, Yongfei Pu, Shiliang Institute of Artificial Intelligence Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Beijing Key Laboratory of Digital Media Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hikvision Research Institute Hangzhou China

Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Cloud sphere: A 3D Shape representation via progressive deformation

arXiv

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arXiv 2021年

作者： Wang, Zongji Liu, Yunfei Lu, Feng Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518000 China

In the area of 3D shape analysis, the geometric properties of a shape have long been studied. Instead of directly extracting representative features using expertdesigned descriptors or end-to-end deep neural networks, this paper is dedicated to discovering distinctive information from the shape formation process. Concretely, a spherical point cloud served as the template is progressively deformed to fit the target shape in a coarse-to-fine manner. During the shape formation process, several checkpoints are inserted to facilitate recording and investigating the intermediate stages. For each stage, the offset field is evaluated as a stage-aware description. The summation of the offsets throughout the shape formation process can completely define the target shape in terms of geometry. In this perspective, one can derive the point-wise shape correspondence from the template inexpensively, which benefits various graphic applications. In this paper, the Progressive Deformation-based Auto-Encoder (PDAE) is proposed to learn the stage-aware description through a coarseto-fine shape fitting task. Experimental results show that the proposed PDAE has the ability to reconstruct 3D shapes with high fidelity, and consistent topology is preserved in the multi-stage deformation process. Addi-tional applications based on the stage-aware description are performed, demonstrating its universality. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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