检索结果-南通市图书馆

Meta ordinal regression Forest for Medical Image Classification With ordinal Labels

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1233-1247页

作者： Yiming Lei Haiping Zhu Junping Zhang Hongming Shan Shanghai Key Laboratory of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200433China with Huawei Shanghai 200120China Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433 Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 200031China IEEE

The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)***,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better *** improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning *** merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)***,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding ***,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction ***,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature *** training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian *** results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.

关键词： Terms-Convolutional neural network(CNNs) medical image classification meta-learning ordinal regression random forest

ordinal-Class Core Vector Machine

在线全文

学校读者我要写书评

暂无评论

Journal of Computer Science & Technology 2010年第4期25卷 699-708页

作者：顾彬王建东李涛 Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing University of Information Science and Technology

ordinal regression is one of the most important tasks of relation learning, and several techniques based on support vector machines （SVMs） have also been proposed for tackling it, but the scalability aspect of these approaches to handle large datasets still needs much of exploration. In this paper, we will extend the recent proposed algorithm Core Vector Machine （CVM） to the ordinal-class data, and propose a new algorithm named as ordinal-Class Core Vector Machine （OCVM）. Similar with CVM, its asymptotic time complexity is linear with the number of training samples, while the space complexity is independent with the number of training samples. We also give some analysis for OCVM, which mainly includes two parts, the first one shows that OCVM can guarantee that the biases are unique and properly ordered under some situation; the second one illustrates the approximate convergence of the solution from the viewpoints of objective function and KKT conditions. Experiments on several synthetic and real world datasets demonstrate that OCVM scales well with the size of the dataset and can achieve comparable generalization performance with existing SVM implementations.

关键词： support vector machine ordinal regression ranking learning core vector machine minimum enclosing ball

Visual perception on the architectural elements of the built heritage of a historic temple town： A case study of Kumbakonam, India

在线全文

学校读者我要写书评

暂无评论

Frontiers of Architectural Research 2017年第1期6卷 96-107页

作者： K. Kiruthisa K. Thirumaran Department of Architecture National Institute of Technology Tiruchirappalli Tamilnadu 620015 India

This paper aims to investigate the changes in the virtual perception on the built heritage at the traditional core setttement of Kumbakonam Town at Tamitnadu and to analyze their implica- tions. Specifically, the major objectives of the study are （1） to identify the architectural elements that manifest the built heritage of Kumbakonam Town and （2） to assess the contMbutions of these elements to the changes in the visuat perception of the town. To achieve these objectives, this study adopts an empirical model that analyzes the architectural elements of the buildings in the study area. Direct observations and documentations of 373 buildings are collected to analyze those etements that contribute to the changes in the visual perception on the built heritage of Kumbakonam Town. An ordinary regression model is used to examine the characteristics of the built heritage across the chariot processional route of the town. Several architectural elements, including pitasters, horizontal cornices, arched windows, and ornamental parapets, improve the image of the town. These empirical findings support the policy framework that enhances the visual perception of Kumbakonam Town.

关键词： Visual perception Built heritage ordinal regression Architectural ele-ments Historic town

Basophile:Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Genomics, Proteomics & Bioinformatics 2013年第2期11卷 86-95页

作者： Dong Wang Surendra Dasari Matthew C.Chambers Jerry D.Holman Kan Chen Daniel C.Liebler Daniel J.Orton Samuel O.Purvine Matthew E.Monroe Chang Y.Chung Kristie L.Rose David L.Tabb Department of Biomedical Informatics Vanderbilt University Medical Center Division of Biomedical Statistics and Informatics Mayo Clinic Department of Biochemistry Vanderbilt University Medical Center Biological Sciences Division and Environmental Molecular Sciences Laboratory Pacific Northwest National Laboratory Department of Pharmacology Vanderbilt University Medical Center Vanderbilt-Ingram Cancer Center Vanderbilt University School of Medicine

In shotgun proteomics, database search algorithms rely on fragmentation models to pre- dict fragment ions that should be observed for a given peptide sequence. The most widely used strat- egy （Naive model） is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmen- tation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher- energy collision induced dissociation （HCD） of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% （on average） over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

关键词： Fragmentation Basicity Fragment size ordinal regression

Research on Ranking Support Vector Machine and Prospects

在线全文

学校读者我要写书评

暂无评论

Research on Ranking Support Vector Machine and Prospects