检索结果-内蒙古大学图书馆

Understanding the mechanism of deep learning framework for lesion detection in pathological images with breast cancer

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Hsu, Wei-Wen Chen, Chung-Hao Hoa, Chang Hou, Yu-Ling Gao, Xiang Shao, Yun Zhang, Xueli Wang, Jingjing He, Tao Tai, Yanghong Department of Electrical and Computer Engineering Old Dominion University Norfolk United States Institute of Big Data Technology Bright Oceans Corporation Beijing China Department of Pathology Fifth Medical Center of the PLA General Hospital Beijing China

The computer-aided detection (CADe) systems are developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing missing inspections. Many studies have shown such a CADe system with deep learning approaches outperforms the one using conventional methods that rely on hand-crafted features based on field-knowledge. However, most developers who adopted deep learning models directly focused on the efficacy of outcomes, without providing comprehensive explanations on why their proposed frameworks can work effectively. In this study, we designed four experiments to verify the consecutive concepts, showing that the deep features learned from pathological patches are interpretable by domain knowledge of pathology and enlightening for clinical diagnosis in the task of lesion detection. The experimental results show the activation features work as morphological descriptors for specific cells or tissues, which agree with the clinical rules in classification. That is, the deep learning framework not only detects the distribution of tumor cells but also recognizes lymphocytes, collagen fibers, and some other non-cell structural tissues. Most of the characteristics learned by the deep learning models have summarized the detection rules that can be recognized by the experienced pathologists, whereas there are still some features may not be intuitive to domain experts but discriminative in classification for machines. Those features are worthy to be further studied in order to find out the reasonable correlations to pathological knowledge, from which pathological experts may draw inspirations for exploring new characteristics in diagnosis. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

Forecasting Thailand’s Precipitation with Cascading Model of CNN and GRU

学校读者我要写书评

暂无评论

Forecasting Thailand’s Precipitation with Cascading Model o...

International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in Thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. There are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in Thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. The experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in Thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

On information-theoretic characterizations of markov random fields and subfields

学校读者我要写书评

暂无评论

On information-theoretic characterizations of markov random ...

作者： Yeung, Raymond W. Al-Bashabsheh, Ali Chen, Chao Chen, Qi Moulin, Pierre Institute of Network Coding Chinese University of Hong Kong Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong Big Data and Brain Computing Center Beihang University Beijing100191 China State Key Laboratory of Integrated Service Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States Coordinated Science Laboratory University of Illinois UrbanaIL61801 United States

Let Xi, i ϵ V form a Markov random field (MRF) represented by an undirected graph G = (V,E) , and V′ be a subset of V. We determine the smallest graph that can always represent the subfield Xi, i ϵ V′ as an MRF. Based on this result, we obtain a necessary and sufficient condition for a subfield of a Markov tree to be also a Markov tree. When G is a path so that Xi, i ϵ V form a Markov chain, it is known that the I -Measure is always nonnegative (Kawabata and Yeung in 1992). We prove that Markov chain is essentially the only MRF such that the I -Measure is always nonnegative. By applying our characterization of the smallest graph representation of a subfield of an MRF, we develop a recursive approach for constructing information diagrams for MRFs. Our work is built on the set-theoretic characterization of an MRF (Yeung et al. in 2002). © 1963-2012 IEEE.

关键词： Markov processes

Developing and validating multi-modal models for mortality prediction in COVID-19 patients: A multi-center retrospective study

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wu, Joy Tzung-Yu De La Hoz, Miguel Ángel Armengol Kuo, Po-Chih Paguio, Joseph Alexander Yao, Jasper Seth Dee, Edward Christopher Yeung, Wesley Jurado, Jerry Moulick, Achintya Milazzo, Carmelo Peinado, Paloma Villares, Paula Cubillo, Antonio Varona, José Felipe Lee, Hyung-Chul Estirado, Alberto Castellano, José Maria Celi, Leo Anthony Department of Radiology and Nuclear Medicine Stanford University Palo AltoCA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Anesthesia Critical Care and Pain Medicine Beth Israel Deaconess Medical Center BostonMA United States Big Data Department Fundacion Progreso y Salud Regional Ministry of Health of Andalucia Andalucia Spain Department of Computer Science National Tsing Hua University Hsinchu Taiwan Albert Einstein Medical Center PhiladelphiaPA United States Department of Radiation Oncology Memorial Sloan Kettering Cancer Center New YorkNY United States National University Heart Center National University Hospital Singapore Singapore Hoboken University Medical Center CarePoint Health HobokenNJ United States Centro Integral de Enfermedades Cardiovasculares Hospital Universitario Monteprincipe Grupo Hm Hospitales Madrid Spain Department of Anesthesiology and Pain Medicine Seoul National University College of Medicine Seoul Korea Republic of Centro Nacional de Investigaciones Cardiovasculares Instituto de Salud Carlos Iii Madrid Spain Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States

Background: The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. Purpose: In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. Materials and Methods: The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N=2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N=242) and an academic center in Seoul, Republic of Korea (N=336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. Results: We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day-mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. Conclusion: We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization. © 2021, CC BY.

关键词： Forecasting

Accumulative time-based ranking method to reputation evaluation in information networks

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liao, Hao Liu, Qi-Xin Huang, Ze-Cheng Yeung, Chi Ho Zhang, Yi-Cheng National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen518060 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong Hong Kong Department of Physics University of Fribourg Fribourg Switzerland

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users’ rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Copyright © 2019, The Authors. All rights reserved.

关键词： Information filtering

Harmonic attentive multimodal neural network for movie recommendation

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2020年第2期1550卷

作者： J X Wang Y Guo T M Qi Q F Tang Department of Computer Science and Engineering East China University of Science and Technology Shanghai 200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai 200436 China Shanghai Engineering Research Center of Big Data & Internet Audience Shanghai 200072 China

A multimodal neural network based on harmonic self-attention was proposed for movie recommendation. This method can deal with the multi-source data and learn representations of users and items well. The multimodal neural network mainly consists of three sub-networks, ResNet, Bert, and LSTM. In addition, the harmonic self-attention mechanism can explore the user's preference from the perspective of time. Experimental results show that compared with other three latest recommendation methods, HSMNN shows better performance in terms of HR and NDCG.

关键词：

Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features

学校读者我要写书评

暂无评论

Computational intelligence and neuroscience 2022年第1期2022卷 1658615页

作者： Azita Yazdani Shervan Fekri-Ershad Saeed Jelvay Department of Health Information Management Clinical Education Research Centre Health Human Resources Research Centre School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran. Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran. Big Data Research Center Najafabad Branch Islamic Azad University Najafabad Iran. Department of Health Information Technology Abadan University of Medical Sciences Abadan Iran.

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.

关键词：

Gender Difference in Neurodevelopment Disorders Among Late Preterm Infants: Exploring the Impact of Antenatal Corticosteroid Timing

学校读者我要写书评

暂无评论

Indian journal of pediatrics 2024年第6期91卷 637页

作者： Hao-Wei Chung Chia-Hung Yu Chiao-Yun Huang Fu-Wen Liang Department of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Pediatrics Kaohsiung Municipal Siaogang Hospital Kaohsiung Medical University Kaohsiung Taiwan. Department of Anesthesiology Chi Mei Medical Center Tainan Taiwan. Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan. Department of Public Health College of Health Sciences Kaohsiung Medical University Kaohsiung Taiwan. Department of Public Health College of Health Sciences Kaohsiung Medical University Kaohsiung Taiwan. fwliang@kmu.edu.tw. Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan. fwliang@kmu.edu.tw. Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan. fwliang@kmu.edu.tw.

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing