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Predicting the pathological status of mammographic microcalcifications through a radiomics approach

Intelligent Medicine 2021年第3期1卷 95-103页

作者： Min Li Liyu Zhu Guangquan Zhou Jianan He Yanni Jiang Yang Chen School of Biological Science and Medical Engineering Southeast UniversityNanjingJiangsu 210096China Department of Radiology the First Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210029China Department of Radiology the Second Affiliated Hospital of Nantong UniversityNantongJiangsu 226001China The State Key Laboratory of Bioelectronics Southeast UniversityNanjingJiangsu 210096China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and EngineeringSoutheast UniversityNanjingJiangsu 210096China The Laboratory of Image Science and Technology Key Laboratory of Ministry of EducationSchool of Computer Science and EngineeringSoutheast UniversityNanjingJiangsu 210096China

Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4(training cohort,n=428;independent testing cohort,n=35)in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January ***,837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoostembedded recursive feature elimination technique(RFE),followed by four machine learning-based classifiers to build the radiomics *** Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression(LR)and support vector machine(SVM)yielded better positive predictive value(PPV)/sensitivity(SE),0.904(95%CI,0.865–0.949)/0.946(95%CI,0.929–0.977)and 0.891(95%CI,0.822–0.939)/0.939(95%CI,0.907–0.973)respectively,outperforming their negative predictive value(NPV)/specificity(SP)from 10-fold crossvalidation(10FCV)of the training *** optimal prognostic model was obtained by SVM with an area under the curve(AUC)of 0.906(95%CI,0.834–0.969)and accuracy(ACC)0.787(95%CI,0.680–0.855)from 10FCV against AUC 0.810(95%CI,0.760–0.960)and ACC 0.800 from the testing *** The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.

关键词： Nonpalpable microcalcifications Radiomics Machine learning

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Dynamic layered-graph model for routing in cognitive radio network

Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of ...

引用

Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and technology (Natural Science Edition) 2008年第6期9卷 586-590页

作者： Li, Yang Dong, Yu-Ning Zhao, Hai-Tao College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu Provincial Key Lab. of Computer Information Processing Technology Soochow University Suzhou 215006 China

To analyze routing protocol in cognitive radio(CR) networks effectively, a dynamic routing model is necessary. Dynamic layered graphic(DG) was proposed aiming at the characteristic of dynamic spectrum access in CR networks. In DG model, nodes were split into many sub-nodes, and each sub-node state could denote whether the related channel was available or *** DG model, sub-node state was taken as a stochastic process and the model of cognitive radio network's dynamic topology was set up by analyzing the dynamic characteristic of sub-node state. Simulation results show that in the aspect of the adaptability to CR network's variable SOP and depiction of the topology, the DG model outperforms the layered graph(LG) model, and can adapt well to CR networks environment.

关键词： Cognitive radio

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SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification

arXiv

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arXiv 2024年

作者： Li, Zuoyong Lin, Qinghua Fan, Haoyi Zhao, Tiesong Zhang, David The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China The School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou45000 China The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Shenzhen518172 China

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://***/AlchemyEmperor/SIAVC. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous information. All rights reserved.

关键词： Semantics

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Fully Differentiable Correlation-Driven 2D/3D Registration for X-Ray to CT Image Fusion

Fully Differentiable Correlation-Driven 2D/3D Registration f...

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IEEE International Symposium on Biomedical Imaging

作者： Minheng Chen Zhirun Zhang Shuheng Gu Zhangyang Ge Youyong Kong School of Computer Science and Engineering Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University China Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. In recent years, some learning-based fully differentiable methods have produced beneficial outcomes while the process of feature extraction and gradient flow transmission still lack controllability and interpretability. To alleviate these problems, in this work, we propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder which enables the network to extract and separate low-frequency global features from high-frequency local features. A correlation-driven loss is further proposed for low-frequency feature and high-frequency feature decomposition based on embedded information. Besides, a training strategy that learns to approximate a convex-shape similarity function is applied in our work. We test our approach on a in-house dataset and show that it outperforms both existing fully differentiable learning-based registration approaches and the conventional optimization-based baseline. Our code is available at https://***/m1nhengChen/cdreg.

关键词： Training Correlation Computed tomography Surgery Process control Feature extraction Controllability

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A Policy-Controlled Signature Scheme for end-to-end Secure Communication

A Policy-Controlled Signature Scheme for end-to-end Secure C...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Feng Yu Linghui Meng Jing Xi Xianxian Li Qianhui Lu Weidong Zhu Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University GuiLin China Jiangsu Provincial Key Laboratory of Netwrok and Information Security University of SouthEast Nanjing China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University GuiLin China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University GuiLin China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

In the digitalized society, end-to-end security communication is receiving increasing attention. Issues such as ma-licious sender impersonation and over-privilege communication continue to prevail in end-to-end communication. This paper pro-poses a policy-controlled signature scheme aimed at addressing these concerns through identity verification and permission man-agement, thus establishing a secure mode of communication. The scheme designs policy-controlled signatures with partial policy hiding functionality, employing expressive access structures and garbled Bloom Filter (GBF) to protect the sender's attributes. In comparison to related approaches, this scheme provides benefits in terms of signature computation cost, thereby enhancing the resilience and efficiency of the communication system against attacks.

关键词： Privacy Costs Communication systems Prevention and mitigation Big Data Computational efficiency Security Protection Resilience

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Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images 38

Leveraging Tumor Heterogeneity: Heterogeneous Graph Represen...

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Wu, Junxian Ke, Xinyi Jiang, Xiaoming Wu, Huanwen Kong, Youyong Shao, Lizhi School of Internet Anhui University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Department of Pathology State Key Laboratory of Complex Severe and Rare Disease Molecular Pathology Research Center Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China School of Bioinformatics Chongqing University of Post and Telecommunications China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Survival prediction is a significant challenge in cancer management. Tumor microenvironment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impact the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods. Source code is available at https://***/wjx-error/ProtoSurv. © 2024 Neural information processing systems foundation. All rights reserved.

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Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration

Embedded Feature Similarity Optimization with Specific Param...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Minheng Chen Zhirun Zhang Shuheng Gu Youyong Kong School of Computer Science and Engineering Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Nanjing China

We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard. The framework we design includes a parameter specification module to efficiently choose initialization pose parameter and a fine-registration module to align images. The proposed framework takes extracting multi-scale features into consideration using a novel composite connection encoder with special training techniques. We compare the method with both learning-based methods and optimization-based methods on a in-house CT/X-ray dataset as well as simulated data to further evaluate performance. Our experiments demonstrate that the method in this paper has improved the registration performance, and thereby outperforms the existing methods in terms of accuracy and running time. We also show the potential of the proposed method as an initial pose estimator. The code is available at https://***/m1nhengChen/SOPI

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LasHeR: A large-scale high-diversity benchmark for RGBT tracking

arXiv

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arXiv 2021年

作者： Li, Chenglong Xue, Wanlin Jia, Yaqing Qu, Zhichen Luo, Bin Tang, Jin Sun, Dengdi Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a Large-scale High-diversity benchmark for short-term RGBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: https://***/mmic-lcl/Datasets-and-benchmark-code. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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NEAT-Net: An Unsupervised Cross-View Prior Inpainting Network for CBCT Metal Artifact Reduction

NEAT-Net: An Unsupervised Cross-View Prior Inpainting Networ...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yang Yang Zhan Tong Xinyun Zhong Yijie Shi Yan Xi Wenxue Yu Zhan Wu Yang Chen Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intraoperative Cone-Beam Computed Tomography (CBCT) facilitates intraoperative navigation for Minimally Invasive Spine Surgery (MISS). However, high-attenuation metal implants used in MISS often cause metal artifacts in the reconstructed CBCT images. Current algorithms do not consider the cross-view information in the projection-domain for metal artifact reduction (MAR). Inaccurate projection-domain inpainting results in CBCT MAR lead to tissue blurring and secondary artifacts, significantly compromising the accuracy of CBCT-guided MISS and increasing surgical risks. To address the above challenge, in this paper, we propose a novel unsupervised cross-view prior inpainting network for CBCT Metal Artifact Reduction named NEAT-Net. Firstly, a cross-view prior multi-scale inpainting module is constructed to learn the inter-view complementary information. Secondly, a hybrid feature attention module is proposed to adaptively fuse cross-view features. In addition, an unsupervised training approach is proposed to directly learn from metal-affected data. Extensive experiments are conducted to verify the effectiveness of our algorithm on a real clinical dataset.

关键词： Training Minimally invasive surgery Accuracy Navigation Fuses Computed tomography Metals Implants Engineering in medicine and biology Image reconstruction

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