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Fully Deep learning for Slit-Lamp Photo Based Nuclear Cataract Grading 22nd

Fully Deep Learning for Slit-Lamp Photo Based Nuclear Catara...

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Xu, Chaoxi Zhu, Xiangjia He, Wenwen Lu, Yi He, Xixi Shang, Zongjiang Wu, Jun Zhang, Keke Zhang, Yinglei Rong, Xianfang Zhao, Zhennan Cai, Lei Ding, Dayong Li, Xirong Renmin Univ China DEKE MOE Key Lab Beijing Peoples R China Renmin Univ China Sch Informat AI & Media Comp Lab Beijing Peoples R China Visionary Intelligence Ltd Vistel AI Lab Beijing Peoples R China Fudan Univ Eye & ENT Hosp Shanghai Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9783030322519;9783030322502

Age-related cataract is a priority eye disease, with nuclear cataract as its most common type. This paper aims for automated nuclear cataract grading based on slit-lamp photos. Different from previous efforts which rely on traditional feature extraction and grade modeling techniques, we propose in this paper a fully deep learning based solution. Given a slit-lamp photo, we localize its nuclear region by Faster R-CNN, followed by a ResNet-101 based grading model. In order to alleviate the issue of imbalanced data, a simple batch balancing strategy is introduced for improving the training of the grading network. Tested on a clinical dataset of 157 slit-lamp photos from 39 female and 31 male patients, the proposed solution outperforms the state-of-the-art, reducing the mean absolute error from 0.357 to 0.313. In addition, our solution processes a slit-lamp photo in approximately 0.1 s, which is two order faster than the state-of-the-art. With its effectiveness and efficiency, the new solution is promising for automated nuclear cataract grading.

关键词： Nuclear cataract grading Slit-lamp photos Deep learning

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The Civil Affairs Information Matrix: Designing Context-Aware Visual Analytics Enabling Mission Planning with Ensemble learning 2nd

The Civil Affairs Information Matrix: Designing Context-Awar...

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2nd international conference on intelligent Human Systems Integration (IHSI) - Integrating People and intelligent

作者： Mullins, Ryan Ford, Benjamin Kemmet, Lynndee Weissman, Shana Aptima Inc 12 Gill St Suite 1400 Woburn MA 01801 USA US Mil Acad West Point NY 10996 USA

ISBN: (纸本)9783030110512;9783030110505

With the pivot towards grey zone operations, the United States Army must engage, influence, and partner with local populations in order to achieve the military objectives of their foreign policy. However, the knowledge that enables successful partnerships is split between the Civil Affairs and Intelligence staff elements. Here, we present the Civil Affairs Information Matrix, a contextaware visual analytics capability enabling automated gap analysis for grey zone operations using fused Civil Affairs and Intelligence data. The Civil Affairs Information Matrix uses semi-supervised and unsupervised learning techniques to fuse and annotate data (for example, surveys and news reports), generate potential actions from this data to address gaps and leverage strengths, and visualize the annotated data and rank-ordered potential actions to planners for consideration. Our ensemble approach enables more collaborative planning processes for grey zone operations that will ensure appropriate and dynamic responses to local population interests while achieving the planned outcomes.

关键词： Visual analytics Context-aware systems Ensemble modeling Civil-military operations Mission planning intelligent decision support

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AI-Aided Game: Enhancing the Defense Performance of Scale-Free Network via Deep Reinforcement learning

AI-Aided Game: Enhancing the Defense Performance of Scale-Fr...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Ziyue Lu Guoming Tang Baochao Chen Bangbang Ren Sheng Chen Deke Guo College of Intelligence and Computing Tianjin University Tianjin China Peng Cheng Laboratory Shenzhen China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728176505

Scale-free network, as a type of complex network, is prone to various target attacks. Some researches characterize the attack and defense problem of complex network as a game problem, and design the optimal defense strategy from the perspective of achieving the game equilibrium. However, such a game problem in the scale-free network space is very difficult to solve because the strategy space is too large. An effective way to achieve the equilibrium is to train a growing population of player strategies and use heuristic methods to guide the population evolution. The deep reinforcement learning (DRL) has been used to guide the population evolution due to its strong ability to explore new strategies. Inspired by this fact, in this paper we apply an AI-aided game framework, Policy Space Response Oracle (PSRO), to solve the above game problem. We first abstract the scale-free network as a game environment and design a set of game rules. The confrontation game occurs between two agents acting as the attacker and the defender in the network, respectively. We then use the convolutional neural network (CNN) to evaluate values of actions in deep Q-network (DQN) and extract game states into tensor forms as CNN's inputs. To tackle the targeted attacks against the scale-free networks, we carefully design a specific action value for the defender combining the node weight and network connectivity parameters of a node in CNN. The simulation results show that the defender agent using CNN as strategies can delay the attacker's intrusions under equilibrium, and achieve a balance between protecting high-weight nodes and keeping the network connectivity.

关键词： Tensors Simulation High performance computing Sociology Games Reinforcement learning Complex networks

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CCIED: Cache-aided Collaborative Intelligence Between Edge Devices

CCIED: Cache-aided Collaborative Intelligence Between Edge D...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Chuanwen Hu Yuebin Bai Rui Wang Chang Liu Xiaolin Wang School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781728176505

Recently, deep learning technology has shined in the fields of computer vision, natural language processing and speech recognition, and related products have sprung up like mushrooms. Due to the storage and calculation of deep neural network (DNN) models are relatively large and mobile edge devices are often resource-constrained, how to efficiently deploy DNN models on resource-constrained edge devices has attracted great attention from academia and industry. There's strength in numbers, so we propose CCIED, a framework which lets edge devices cooperate with each other to complete DNN inference tasks. Due to task inputs in the mobile edge computing scenarios usually have great similarities, the outputs of the middle layer of the neural network and the corresponding labels are cached. When a similar input already exists in the cache, the device does not need to perform the remaining calculations, but directly returns the cached results. One of the challenges of collaborative inference is that the communication overhead associated with transferring intermediate data can be significant. We therefore perform weight pruning only on the layer that obtains the intermediate results, which can greatly reduce the redundant parameters of the intermediate results, thereby reducing the time for transferring data between devices, and basically does not reduce the complexity of the model. Experimental results show that CCIED can efficiently deploy the DNN model on edge devices with almost no loss of precision, and can significantly reduce the total latency during cache hits.

关键词： Performance evaluation Industries Runtime Computational modeling High performance computing Neural networks Collaboration

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The Impact of Digital Transformation on Technological Trends regarding IoT and Smart Systems 22

The Impact of Digital Transformation on Technological Trends...

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22nd international conference "Enterprise engineering and Knowledge Management", EEKM 2019

作者： Tropmann-Frick, Marina Hamburg University of Applied Sciences Hamburg Germany

The present time is the time of digital transformation. New smart or intelligent devices and technologies influence our everyday life and have a great impact on our environment. Such paradigms as Internet of Things (IoT) and Smart Systems are also unavoidable undergoing changes and modifications. Especially the domain of Smart Cities is getting more and more attention and is benefiting from the outcomes of digital transformation. In this paper we focus on the evolution of technologies in this fields and provide an overview over a selection of numerous applications. © 2019 CEUR-WS. All rights reserved.

关键词： Big data

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Emergency Clinical Procedure Detection With Deep learning

Emergency Clinical Procedure Detection With Deep Learning

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Annual international conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Lingfeng Li Richard A. Paris Conner Pinson Yan Wang Joseph Coco Jamison Heard Julie A. Adams Daniel V. Fabbri Bobby Bodenheimer Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee Vanderbilt University Medical Center Nashville Tennessee Department of Electrical and Microelectronic Engineering Rochester Institute of Technology Rochester NY Collaborative Robotics and Intelligent Systems Institute Oregon State University Oregon

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care. In this paper we explore the use convolutional neural networks (CNNs) on raw video data to determine how well video data alone can automatically identify clinical procedures. We apply multiple deep learning models to this problem, with significant variation in results. Our findings indicate performance improvements compared to prior work, but also indicate a need for more training data to reach clinically deployable levels of success.

关键词： Adaptation models Training Testing Machine learning Hospitals Cameras

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Distributed Systems Education: from Traditional Models to New Paths of learning 22

Distributed Systems Education: from Traditional Models to Ne...

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22nd international conference on Control Systems and Computer Science (CSCS)

作者： Pop, Florin Cristea, Valentin Univ Politehn Bucuresti Comp Sci Dept Bucharest Romania Natl Inst Res & Dev Informat ICI Bucharest Romania

ISBN: (纸本)9781728123318

Distributed systems underpin our digital economy and thus the modern world. By operating as enormous yet largely autonomous ecosystems of complex technologies, these systems already store and process a majority of the (big) data produced by our industry and our society, and host the global cloud market. Teaching and researching distributed systems must enable the next killer applications of the next-generation systems: to cope with the new applications (multimedia, satellite images, government, health, critical infrastructure) expected to represent over 80% of the overall Internet traffic by 2020, enabling much more distributed and heterogeneous infrastructure elements (e.g., clouds combined with fog and edge computing, pervasive system, ubiquitous and mobile computing). This extended abstract is focused on presenting the main ideas about teaching distributed system for undergraduate, master and PhD students, starting from achievements obtained by Computer Science and engineering Department of University Politehnica of Bucharest (UPB) and considering success collaborations with VU Amsterdam, different universities from Europe and USA and other research institutes (e.g. Cern, INRIA).

关键词： Distributed Systems Complexity Microservices Processing Storage Applications Heterogeneity

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Hybrid Feature Fusion Using RNN and Pre-trained CNN for Classification of Alzheimer's Disease (Poster) 22

Hybrid Feature Fusion Using RNN and Pre-trained CNN for Clas...

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22nd international conference on Information Fusion, FUSION 2019

作者： Jabason, Emimal Omair Ahmad, M. Swamy, M.N.S. Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada

ISBN: (纸本)9780996452786

The accurate classification of AD is very essential for both patient and social care, and it will be more significant once the treatment options are available to reverse the progress of the disease. The recent success of deep learning techniques has rapidly advanced the automatic classification of AD using neuroimaging biomarkers such as MRI. However, there exist two major challenges. First, training a deep convolutional neural network (CNN) from scratch relies on a large number of labeled training data to obtain high accuracy without overfitting. Second, due to high computational cost, most of the existing techniques employ 2D CNN that cannot leverage the complete spatial information;hence, it loses the inter-slice correlation. To address these limitations, we combine a recurrent neural network (RNN), specifically long short-term memory (LSTM) on top of the bottleneck layer of pre-trained DenseNet architecture, a deep CNN has already been trained on a large-scale dataset called ImageNet. In addition to the intra-slice features extracted from the deep CNN, the proposed technique exploits the inter-slice features through LSTM in order to discriminate the patients having AD and cognitively normal (CN) clinical status from the brain MRI data. Through experimental results, we show that our proposed model has better performance than state-of-the-art deep learning methods on the Open Access Series of Imaging Studies (OASIS) dataset using 5-fold cross validation. © 2019 ISIF-international Society of Information Fusion.

关键词： Magnetic resonance imaging

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The Prevalence of Errors in Machine learning Experiments 1

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Shepperd, Martin Guo, Yuchen Li, Ning Arzoky, Mahir Capiluppi, Andrea Counsell, Steve Destefanis, Giuseppe Swift, Stephen Tucker, Allan Yousefi, Leila Brunel Univ London London England Xi An Jiao Tong Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.

关键词： Classifier Computational experiment Reliability Error

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Selectively oversampling difficult positive samples from imbalanced data for preprocessing 22

Selectively oversampling difficult positive samples from imb...

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22nd international conference on Computer and Information Technology, ICCIT 2019

作者： Mahin, Md. Rukhsara, Lamia Kabir, Md. Yasin Mostafizur Rahman, H.M. Islam, Md Jahidul Khatun, Ayesha Kabir, Sumaiya Green University of Bangladesh Dept. of Computer Science and Engineering Bangladesh Daffodil International University Dept. of Computer Science and Engineering Bangladesh Noakhali Science and Technology University Dept. of Computer Science and Engineering Bangladesh Ahsanullah University of Science and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781728158426

Oversampling is a procedure traditionally has been applied to train machine learning classifiers for a better performance in presence of class imbalance. This work suggests a new insight for oversampling imbalanced data. In literature Borderline samples are mainly focused for oversampling. How-ever, because of low number of samples within the positive class a huge percentage of samples can be labeled as Rare and Outliers. These samples are often overlooked by the traditional oversampling methods or the nearest negative samples are often removed to increase positive prediction rate- while sacrificing the negative prediction rate. This work demonstrates that by only oversampling the Borderline, Rare and Outlier samples at different rate, better performance can be achieved than all other pre-processing methods. The proposed method is applied on four datasets- Abalone, CMC, Solar Flare and Seismic Bump, collected from the UCL digital library and compared with four traditional pre-processing methods ADYSYN, SMOTE, Border-line SMOTE 1 and 2 from imbalanced learn toolkit python. The result analysis shows that with fine tuning better performance can be achieved for all known performance measurements: Accuracy, True Positive Rate, True Negative Rate, Geometric Mean, Area Under the Curve measure and F-measure. © 2019 IEEE.

关键词： Digital libraries

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