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Efficient Regression with Feature Selection Based on L12-Norm

Efficient Regression with Feature Selection Based on L12-Nor...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Yu Lei Qin Li Jane You School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China School of Telecommunications Engineering Xidian University Xi’an China Department of Computing The Hong Kong Polytechnic University Hong Kong China

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with ℓ 12 -norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by ℓ 12 -norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the ℓ 12 -norm regularization iteratively. Experimental results demonstrate the effectiveness of our method.

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Semantic Filtering Global and Local Embeddings Fusion for Few-Shot Image Classification 23

Semantic Filtering Global and Local Embeddings Fusion for Fe...

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proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Luyu Nong Min Wu Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9798400707988

Neural networks have demonstrated superior performance in image classification tasks, however, acquiring sufficient labeled data for training them in real-world scenarios remains challenging. Few-shot learning (FSL) was proposed to address this challenge in low-data scenarios and has attracted considerable attention in recent years. In few-shot image classification tasks, most of the existing FSL methods focus on designing discriminative network structures for exploring image features, either neglecting semantic information that is crucial for accurate classification, or tending to prioritize either global or local image features. To address this issue, we propose in this work a novel model Semantic Filtering Global and Local embeddings for fusion (SFGL) for few-shot image classification. SFGL effectively integrates both global and local features while leveraging semantic information. Extensive experiments conducted on benchmark datasets demonstrate that our proposed SFGL model outperforms state-of-the-art methods in FSL tasks.

关键词： few-shot learning global-local fusion image classification metric-based method semantic filter

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Integrating Autoencoder-Based Hybrid Models into Cervical Carcinoma Prediction from Liquid-Based Cytology

Integrating Autoencoder-Based Hybrid Models into Cervical Ca...

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12th international conference on Data Science, Technology and Applications, DATA 2023

作者： Idlahcen, Ferdaous Idri, Ali Zerouaoui, Hasnae Al Khwarizmi College of Computing Mohammed VI Polytechnic University Ben Guerir43150 Morocco Software Project Management Research Team ENSIAS Mohammed V University Rabat10000 Morocco

ISBN: (纸本)9789897586644

Artificial intelligence (AI)-assisted cervical cytology is poised to enhance sensitivity whilst lessening bias, labor, and time expenses. It typically involves image processing and deep learning to automatically recognize pre-cancerous lesions on a given whole-slide image (WSI) prior to lethal invasive cancer development. Here, we introduce autoencoder (AE)-based hybrid models for cervical carcinoma prediction on the Mendeleyliquid-based cytology dataset. this is built on fourteen combinations of AE, DenseNet-201, and six state-ofthe-art classifiers: adaptive boosting (AdaBoost), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), k-nearest neighbors (k-NN), and random forest (RF). As empirical evaluations, four performance metrics, Scott-Knott (SK), and Borda count voting scheme, were performed. the AE-based hybrid models integrating AdaBoost, MLP, and RF as classifiers are among the top-ranked architectures, with respective accuracy values of 99.30, 99.20, and 98.48%. Yet, DenseNet-201 remains a solid option when adopting an end-to-end training strategy. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Support vector machines

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Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT

Personalized Modeling and Assessment of the Aortic-Mitral Co...

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12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Ionasec, Razvan Ioan Voigt, Ingmar Georgescu, Bogdan Wang, Yang Houle, Helene Hornegger, Joachim Navab, Nassir Comaniciu, Dorin Siemens Corp Res Integrated Data Syst Princeton NJ USA Siemens Corp Technol Software & Engn Erlangen Germany Ultrasound Siemens Med Solut Mountain View CA USA Tech Univ Munich Comp Aided Med Procedures D-80290 Munich Germany Friedrich Alexander Univ Chair Pattern Recognit Erlangen Germany

ISBN: (纸本)9783642042706

the anatomy, function and hemodynamics of the aortic and mitral valves are known to be strongly interconnected. An integrated quantitative and visual assessment of the aortic-mitral coupling may have an impact on patient evaluation, planning and guidance of minimal invasive procedures. In this paper, we propose a novel model-driven method for functional and morphological characterization of the entire aortic-mitral apparatus. A holistic physiological model is hierarchically defined to represent the anatomy and motion of the two left;heart;valves. Robust learning-based algorithms are applied to estimate the patient-specific spatial-temporal parameters from four-dimensional TEE and CT data. the piecewise affine location of the valves is initially determined over the whole cardiac cycle using an incremental search performed in marginal spaces. Consequently, efficient spectrum detection in the trajectory space is applied to estimate the cyclic motion of the articulated model. Finally, the full personalized surface model of the aortic-mitral coupling is constructed using statistical shape models and local spatial-temporal refinement. Experiments performed on 65 4D TEE and 69 4D CT sequences demonstrated an average accuracy of 1.45mm and speed of 60 seconds for the proposed approach. Initial clinical validation oil model-based and expert measurement showed the precision to be in the range of the inter-user variability. To the best of our knowledge this is the first time a complete model of the aortic-mitral coupling estimated from TEE and CT data is proposed.

关键词： Physiological models

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A Statistical Model of Right Ventricle in Tetralogy of Fallot for Prediction of Remodelling and therapy Planning

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12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Mansi, Tommaso Durrleman, Stanley Bernhardt, Boris Sermesant, Maxime Delingette, Herve Voigt, Ingmar Lurz, Philipp Taylor, Andrew M. Blanc, Julie Boudjemline, Younes Pennec, Xavier Ayache, Nicholas INRIA Mediterranee ASCLEPIOS Project Sophia Antipolis France McGill Univ Montreal Neurol Inst Neuroimaging Epilepsy Lab Quebec City PQ Canada Siemens AG CT SE SCR2 5 Erlangen Germany Univ Erlangen Nurnberg Chair Pattern Recognit Erlangen Germany UCL Inst Child Hlth & Great Ormand St Hosp Children London England Hop Necker Enfants Malad Serv Cardiol Pediat Paris France

ISBN: (纸本)9783642042676

Patients with repaired Tetralogy of Fallot commonly suffer from chronic pulmonary valve regurgitations awl extremely dilated right ventricle (RV). To reduce risk factors, new pulmonary valves must be re-implanted. However, establishing the best timing for re-intervention is a, clinical challenge because of the large variability in RV shape and in pathology evolution. this study aims at quantifying the regional impacts of growth and regurgitations upon the end-diastolic RV anatomy. the ultimate coal is to determine, among clinical variables, predictors for the shape in order to build a statistical model that predicts RV remodelling. the proposed approach relies on a forward model based on currents and LDDMM algorithm to estimate an unbiased template of 18 patients and the deformations towards each individual shape. Cross-sectional multivariate analyses are carried out to assess the effects of body surface area, tricuspid and transpulmonary valve regurgitations upon the RV shape. the statistically significant deformation modes were found clinically relevant. Canonical correlation analysis yielded a generative model that was successfully tested on two new patients.

关键词： Deformation

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Inverse C-arm Positioning for Interventional Procedures Using Real-Time Body Part Detection

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12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Schaller, Christian Rohkohl, Christopher Penne, Jochen Stuermer, Michael Hornegger, Joachim Univ Erlangen Nurnberg Chair Pattern Recognit Martensstr 3 D-91058 Erlangen Germany Univ Erlangen Nurnberg Erlangen Grad Sch Adv Optical D-91058 Erlangen Germany

ISBN: (纸本)9783642042676

the automation and speedup of interventional therapy and diagnostic workflows is a crucial issue. One way to improve these workflows is to accelerate the image acquisition procedures by fully automating the patient setup. this paper describes a system that performs this task without the use of markers or other prior assumptions. It returns metric coordinates of the 3-D body shape in real-time for inverse positioning. this is achieved by the application of an emerging technology, called Time-of-Flight (ToF) sensor. A ToF sensor is a cost-efficient, off-the-shelf camera which provides more than 40,000 3-D points in real-time. the first contribution of this paper is the incorporation of this novel imaging technology (ToF) in interventional imaging. the second contribution is the ability of a C-arm system to position itself with respect to the patient prior to the acquisition. We are using the 3-D surface information of the patient to partition the body into anatomical sections. this is achieved by a fast two-stage classification process. the system computes the ISO-center for each detected region. To verify our system we performed several tests on the ISO-center of the head. Firstly, the reproducibility of the head ISO-center computation was evaluated. We achieved an accuracy of (x: 1.73+/-1.11 mm/y: 1.87+/-1.31 mm/z: 2.91+/-2.62 mm). Secondly, a C-arm head scan of a body phantom was setup. Our system automatically aligned the ISO-center of the head with the C-arm ISO-center. Here we achieved an accuracy of +/- 1cm, which is within the accuracy of the patient table control.

关键词： Image enhancement

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Parallelizing abnormal event detection in crowded scenes with GPU

Parallelizing abnormal event detection in crowded scenes wit...

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international conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Mohammadreza Yavari Maozhen Li Siguang Li Man Qi Department of Electronic and Computer Engineering Brunel University London Uxbridge UB8 3PH UK The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China

Analyzing human activities in surveillance videos a challenging task due to the high volume of data that needs to be processed in a timely manner. this paper presents a GPU based Gaussian Mixture Model (GMM) to detect abnormal activities in crowded scenes. GMM is a fully unsupervised method that predicts abnormal crowd behaviors based on the processing of normal crowd behaviors. Specifically, we use crowd distribution and GMM to estimate the speed and to predict the behaviors of the crowd. the performance of the parallel GMM is evaluated from the aspects of computation efficiency and accuracy in terms of area under the curve.

关键词： Videos Graphics processing units Computational modeling Computer vision Surveillance Image motion analysis pattern recognition

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Fast and Robust 3-D MRI Brain Structure Segmentation

Fast and Robust 3-D MRI Brain Structure Segmentation

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12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Wels, Michael Zheng, Yefeng Carneiro, Gustavo Huber, Martin Hornegger, Joachim Comaniciu, Dorin Univ Erlangen Nurnberg Dept Comp Sci Chair Pattern Recognit D-8520 Erlangen Germany Integrated Data Syst Siemens Corp Res Princeton NJ USA Univ Tecn Lisboa Inst Syst & Robot Dept Elect & Comp Engn Lisbon Portugal Siemens CT SE5 SCR2 Erlangen Germany

ISBN: (纸本)9783642042706

We present a novel method for the automatic deflection and segmentation of (sub-)cortical gray matter structures in 3-D magnetic resonance images of the human brain. Essentially, the method is a top-down segmentation approach based on the recently introduced concept Of Marginal Space Learning (MSL). We show that MSL naturally decomposes the parameter space of anatomy shapes along decreasing levels of geometrical abstraction into subspaces of increasing dimensionality by exploiting parameter invariance. At each level of: abstraction, i.e., in each subspace, we build strong discriminative models from annotated training data, and use these models to narrow the range of possible solutions until a final shape can be inferred. Contextual information is introduced into the system by representing candidate shape parameters with high-dimensional vectors of 3-D generalized Haar features and steer-able features derived from the observed volume intensities. Cur system allows us to detect and segment 8 (sub-)cortical gray matter Structures in T1-weighted 3-D MR brain scans from a variety of different scanners in on average 13.9 sec., which is faster than most of the approaches in the literature. In order to ensure comparability of the achieved results and to validate robustness, we evaluate our method on two publicly available gold standard databases consisting of several TA-weighted 3-D brain AIR scans from different;scanners and sites. the proposed method achieves an accuracy better than most state-of-the-art approaches using standardized distance and overlap metrics.

关键词： Magnetic resonance imaging

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Personalized Pulmonary Trunk Modeling for Intervention Planning and Valve Assessment Estimated from CT Data

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12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Vitanovski, Dime Ionasec, Razvan Ioan Georgescu, Bogdan Huber, Martin Taylor, Andrew Mayall Hornegger, Joachim Comaniciu, Dorin Siemens Corp Res Integrated Data Syst Princeton NJ USA Siemens Corp Technol Software & Engn Erlangen Germany Friedrich Alexander Univ Chair Pattern Recognit Erlangen Germany UCL Inst Child Hlth London England UCL Great Ormond Street Hosp Children London England Tech Univ Munich Comp Aided Med Procedures Munich Germany

ISBN: (纸本)9783642042676

Pulmonary valve disease affects a, significant;portion of the global population and often occurs in conjunction with other heart dysfunctions. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. As minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. III this papers we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. An abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. A hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric CT. scans. the algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. the estimated personalized model enables for efficient and precise quantification of Function and morphology. this ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. Experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. An initial clinical validation yielded a significant correlation between model-based and expert measurements. To the best of our knowledge this is the first;dynamic model of the pulmonary trunk and right ventricle outflow track estimated from CT data.

关键词： Physiological models

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CDHT: the design of a clustered DHT routing scheme for serverless (P2P) networks

CDHT: the design of a clustered DHT routing scheme for serve...

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IEEE international conference on Networks

作者： W. Ye A.I. Khan E.A. Kendall School of Network Computing Monash University Frankston VIC Australia

Avoiding hot spots and handling dynamic network environments are the inherent weaknesses of the DHT-based (dynamic hash table) p2p networks. the solution lies in isolating the DHT overlay from the network uncertainty and seeking an efficient and complementary lookup pattern outside the DHT. We present the design of a hybrid-overlay architecture for p2p networks in this paper that forms the DHT overlay based on the serverless layer clusters. the architecture delegates the handling of the network uncertainty to the network-aware serverless Layer; thus a more reliable network and content structure is constructed as the foundation of the DHT overlay. In addition, a new heuristic approach to content location is made possible to exploit network proximity and content popularity to balance network traffic and to improve the lookup efficiency for popular content.

关键词： Routing Peer to peer computing Uncertainty Telecommunication traffic Scalability Load management Computer networks Information technology Australia Joining processes

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