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COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification ***: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected ***: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest X-ray contagious infection

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Channel Estimation By Transmitting Pilots From Reconfigurable Intelligent Surface

arXiv

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arXiv 2023年

作者： Zhu, Yanze Liu, Yang Wu, Qingqing You, Changsheng Shi, Qingjiang The School of Information and Communication Engineering Dalian University of Technology Dalian China The Department of Electronic Engineering Shanghai Jiao Tong University 200240 China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The School of Software Engineering Tongji University Shanghai China The Shenzhen Research Institute of Big Data Shenzhen China

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Channel estimation (CE) of RIS device is a critical but also challenging issue for its development. The mainstream of existing CE methods is confined to the so-called cascaded channel (CscdChn) estimation scheme, which treats the multiplicative two-hop RIS channels as an effective one and measures it as a whole. This CscdChn training method suffers from severe double-fading attenuation loss, which significantly degrades the CE accuracy. In this paper, we propose a novel RIS-transmitting (RIS-TX) based CE scheme, which has lower pilot overhead than CscdChn scheme and effectively overcomes the double-fading curse via incorporating only one single transmit radio frequency (RF)-chain into RIS. We develop highly efficient gradient descent (GD) and penalty duality decomposition (PDD)-based solutions to resolve the pilot design task for the RIS-TX CE scheme, which is a difficult quartic optimization problem. Our designed pilot signal outperforms the discrete Fourier transform (DFT) sequence, which is reported to be optimal for CscdChn scheme. Besides, both theoretical analysis and numerical results demonstrate that our proposed RIS-TX scheme exhibits distinct performance characteristics as opposed to its CscdChn counterpart and yields superior accuracy when RIS device is not extremely large. Copyright © 2023, The Authors. All rights reserved.

关键词： Design for testability

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A study on the deviations in performance of FNNs and CNNs in the realm of grayscale adversarial images

arXiv

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arXiv 2022年

作者： Nagabushanam, Durga Shree Mathew, Steve D.A. Chowdhary, Chiranji Lal Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India Data Science and Applications Indian Institute of Technology Madras Chennai600036 India

Neural Networks are prone to having lesser accuracy in the classification of images with noise perturbation. Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. But our study shows that they are extremely vulnerable to noise addition while Feed-forward Neural Networks, FNNs show very less correspondence with noise perturbation, maintaining their accuracy almost undisturbed. FNNs are observed to be better at classifying noise-intensive, single-channeled images that are just sheer noise to human vision. In our study, we have used the hand-written digits dataset, MNIST with the following architectures: FNNs with 1 and 2 hidden layers and CNNs with 3, 4, 6 and 8 convolutions and analyzed their accuracies. FNNs stand out to show that irrespective of the intensity of noise, they have a classification accuracy of more than 85%. In our analysis of CNNs with this data, the deceleration of classification accuracy of CNN with 8 convolutions was half of that of the rest of the CNNs. Correlation analysis and mathematical modelling of the accuracy trends act as roadmaps to these conclusions. © 2022, CC BY.

关键词： Convolutional neural networks

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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International Conference on data engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

arXiv

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arXiv 2024年

作者： Wan, Yuanyu Wei, Tong Xue, Bo Song, Mingli Zhang, Lijun School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering Southeast University Nanjing China Department of Computer Science City University of Hong Kong Hong Kong National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using only local computations and communications. Previous studies have established O(n5/4ρ−1/2√T) and O(n3/2ρ−1 log T) regret bounds for convex and strongly convex functions respectively, where n is the number of local learners, ρ √T) for convex functions and Ω(n) for strongly convex functions. To fill these gaps, in this paper, we first develop a novel D-OCO algorithm that can respectively reduce the regret bounds for convex and strongly convex functions to Õ(nρ−1/4√T) and Õ(nρ−1/2 log T). The primary technique is to design an online accelerated gossip strategy that enjoys a faster average consensus among local learners. Furthermore, by carefully exploiting spectral properties of a specific network topology, we enhance the lower bounds for convex and strongly convex functions to Ω(nρ−1/4√T) and Ω(nρ−1/2 log T), respectively. These results suggest that the regret of our algorithm is nearly optimal in terms of T, n, and ρ for both convex and strongly convex functions. Finally, we propose a projection-free variant of our algorithm to efficiently handle practical applications with complex constraints. Our analysis reveals that the projection-free variant can achieve O(nT 3/4) and O(nT 2/3(log_ T)1/3) regret bounds for convex and strongly convex functions with nearly optimal Õ(ρ−1/2√T) and Õ(ρ−1/2T 1/3(log T)2/3) communication rounds, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Evaluation of the Relevance of Adverse Drug Reactions Based on ERNIE-DPCNN

Evaluation of the Relevance of Adverse Drug Reactions Based ...

引用

IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Hongliang Liu Zhiyu Chen Miao Yu Wenbo Cui Lin Zang Wei Ren Yongfang Hou Center for Drug Reevaluation National Medical Products Administration Beijing China School of Software Engineering North University of China Taiyuan China Pharmacovigilance Research Center for Information Technology and Data Science Cross-Strait Tsinghua Research Institute Xiamen China Department of Automation Research Center for Pharmacovigilance IT and Data Science(PVID) Tsinghua University Beijing China

Adverse drug reactions (ADR) have become a common and serious problem faced by drug users worldwide, posing a significant threat to human life and health safety. How to achieve automatic evaluation of the quality of ADR reports, and how to mine and evaluate the relevance between drugs and adverse drug reactions, has become an urgent problem that needs to be solved at present. In this study, a text classification technology based on deep learning were employed to establish an automated system to evaluate the information quality and relevance of ADR reports, using ADR reports from cooperative medical institutions and case studies in the literature as samples. The ERNIE+DCGNN model was used to train the ADR relevance evaluation model, and its effects were compared with other mainstream models. Comparative experimental results demonstrated that the ADR relevance evaluation model constructed in this paper had better experimental results.

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Establish a smart healthcare system with AIoT for Chinese Medicine

Establish a smart healthcare system with AIoT for Chinese Me...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Chun-Cheng Wei Ching-Wen Chen Lon-Chen Hung School of Software and Big Data Changzhou College of Information Technology Department of Multimedia & Game Science Lunghwa University of Science and Technology Taoyuan Taiwan R.O.C Department of Electronic Engineering Lunghwa University of Science and Technology

ISBN: (纸本)9781665454209

This study establishes an intelligent medical system for diagnosis and treatment of Chinese medicine. Temperature and heart rate were measured using a variety of Internet of things technologies, including the WeMos D1 Mini with ESP8266 integrated and thermistors, and then the resistance was learned to relate to temperature via a neural network. In addition, the AD8232 biomedical sensor and the WeMos D1 Mini were used for ECG measurements and heart rate conversion. A self-written mobile application designed with multiple units to collect data from a variety of patients. The tongue diagnosis unit can recognize the characteristics of tongue substance and coating. The pulse examination unit adopts the five layer pulse method of Yaowangmai pulse. The inquiry unit adopts the syndrome differentiation method of Dr. Shaogong Shen. These IoT data, information if the patient's tongue diagnosis, five-layer pulse examination data and various inquiry data can all be read and transmitted to the cloud. The intelligent medical system of Chinese medicine in this study can also make preliminary treatment recommendations based on a variety of patient data and provide corresponding treatment drugs

关键词： Temperature measurement Temperature sensors Heart rate Drugs Cloud computing Temperature distribution Tongue

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Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors data models Intelligent systems Facial features Principal component analysis Least mean squares methods

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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