检索结果-内蒙古大学图书馆

Advanced Brain Tumor Segmentation With a Multiscale CNN and conditional random fields

IEEE ACCESS 2025年 13卷 34925-34935页

作者： Guennich, Ala Othmani, Mohamed Ltifi, Hela Univ Sfax Natl Engn Sch Sfax Sfax 3029 Tunisia Univ Gafsa Fac Sci Gafsa Gafsa 2112 Tunisia Univ Sfax Fac Sci Sfax Adv Technol Environm & Smart Cities ATES Sfax 3029 Tunisia Univ Kairouan Fac Sci & Technol Sidi Bouzid Sidi Bouzid 3100 Tunisia Univ Sfax REGIM Lab ENIS Sfax 3029 Tunisia

The use of high-precision automatic algorithms for segmenting brain tumors has the potential to improve disease diagnosis, treatment monitoring, and large-scale pathological studies. In this study, we present a novel 9-layer multiscale architecture designed specifically for the semantic segmentation of 3D medical images, with a particular focus on brain tumor images, using convolutional neural networks. Our innovative solution incorporates several significant enhancements, including the use of variable-sized filters between layers and the early incorporation of residual connections from the very first layer. These modifications allow us to reduce the number of layers in our network while significantly improving the accuracy of 3D medical image segmentation. For post-processing the network's soft segmentation, we employ a 3D fully connected conditional random Field, which effectively removes false positives. This approach enables our model to achieve an impressive accuracy of 99.88%, a sensitivity of 99.86%, and a specificity of 99.96%, outperforming existing models. This combination of innovations positions our solution as a major advancement in the field of 3D medical image segmentation, offering an optimal balance between accuracy and efficiency for clinical applications.

关键词： Brain tumors Convolutional neural networks conditional random fields Brain modeling Three-dimensional displays Filters Accuracy Training Medical diagnostic imaging Computer architecture 3D medical imaging brain tumor semantic segmentation residual connections convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Interactive Channel Segmentation From 2-D Seismic Images Using Deep Learning and conditional random fields

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhang, Hao Zhu, Peimin Song, Xianhai Ali, Muhammad Li, Ziang Liao, Zhiying Ruan, Dianyong Li, Tao China Univ Geosci CUG Inst Geophys & Geomatics Hubei Subsurface Multiscale Imaging Key Lab Wuhan 430074 Peoples R China

In the exploration of oil and gas reservoirs, channels are important locations for storing oil and gas, and their distribution in the subsurface is usually heterogeneous. Therefore, interpreting channels from seismic data is very meaningful for oil and gas exploration. Currently, methods for extracting channels from seismic data mainly rely on seismic attributes, edge detection algorithms, and deep learning. However, these methods cannot fully and accurately delineate the boundary details and structural characteristics of channels when faced with poor-quality seismic data. To address this issue, we proposed an interactive interpretation method for 2-D channels based on multiattributes and a convolutional neural network (CNN) that could more accurately identify and segment channel bodies with fuzzy boundaries and poor continuity. First, we selected seed points from seismic data to indicate the presence of channels in the area. To highlight the channel structures and reduce the difficulties in identification, we used the geodesic distance map calculated from the seed points and two seismic attributes commonly used for channel identification as the inputs to the CNN model. Next, the probability map of the channels was output from the CNN model to obtain the preliminary results of the channel recognition. Finally, we judged whether additional seed points needed to be added according to the preliminary results, and we combined the conditional random field (CRF) to fuse the geodesic distance map of the additional points with the probability map of the CNN model, ultimately obtaining accurate channel results. Compared to the automatic CNN method, this method extracted more complete channels and improved the continuity of the channel boundaries. In the case of complex seismic data, this method can effectively interpret channels and has important practical significance.

关键词： Coherence Deep learning Accuracy Data mining Convolutional neural networks Geology Training conditional random fields Rivers Oils Channel interpretation conditional random fields (CRFs) convolutional neural network (CNN) geodesic distance map interactive segmentation method

来源：评论

学校读者我要写书评

暂无评论

Enhancing Out of Vocabulary(OOV) Token representation with Aggregated conditional random fields model for Kannada NER

Enhancing Out of Vocabulary(OOV) Token representation with A...

引用

Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Tashwin SJ Preethi P Mamatha H R Dept of Computer Science PES University

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Named entities are dynamic, as the contexts shift in a language, new entities emerge, reflecting the change over time, but large language models have a fixed vocabulary making it difficult to process out-of-vocabulary words(OOV), there are deep learning methods like BiLSTM, attention mechanism which can be used to get great results for Named entity recognition, but in case of low resource languages like Kannada, data available is not sufficient to train these deep learning models, so the research work attempts to solve the problem of NER using Aggregated conditional random fields. The training is done on ai4bharat/naamapadam dataset which is the largest publicly available dataset for 11 major Indian languages including Kannada. The tokenization is done using a transformer-based multilingual model xlm-roberta-base. In this experiment, the OOV words are split into multiple sub-tokens and then aggregated using an additive pooling layer, and then passed on to the CRF classifier. Results show that the additive pooling model performs better than the base model benchmark and also better than vanilla CRF. Our model was able to produce an f1 score of 78.97 containing around 5728 OOV words in test and 14545 OOV words in the validation set.

关键词： Deep learning Training Vocabulary Additives Named entity recognition Transformers conditional random fields Data models Tokenization Multilingual

来源：评论

学校读者我要写书评

暂无评论

conditional random fields for Korean Morpheme Segmentation and POS Tagging

引用

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2015年第3期14卷 1–16页

作者： Na, Seung-Hoon Busan Univ Foreign Studies Busan South Korea

There has been recent interest in statistical approaches to Korean morphological analysis. However, previous studies have been based mostly on generative models, including a hidden Markov model (HMM), without utilizing discriminative models such as a conditional random field (CRF). We present a two-stage discriminative approach based on CRFs for Korean morphological analysis. Similar to methods used for Chinese, we perform two disambiguation procedures based on CRFs: (1) morpheme segmentation and (2) POS tagging. In morpheme segmentation, an input sentence is segmented into sequences of morphemes, where a morpheme unit is either atomic or compound. In the POS tagging procedure, each morpheme (atomic or compound) is assigned a POS tag. Once POS tagging is complete, we carry out a post-processing of the compound morphemes, where each compound morpheme is further decomposed into atomic morphemes, which is based on pre-analyzed patterns and generalized HMMs obtained from the given tagged corpus. Experimental results show the promise of our proposed method.

关键词： Algorithms Experimentation conditional random fields morpheme segmentation POS tagging Korean morphological analysis

来源：评论

学校读者我要写书评

暂无评论

conditional random fields for entity extraction and ontological text coding

引用

COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY 2008年第3期14卷 248-262页

作者： Diesner, Jana Carley, Kathleen M. Carnegie Mellon Univ Sch Comp Sci Inst Software Res Ctr Computat Anal Social & Org Syst CASOS Pittsburgh PA 15213 USA

Previous research suggests that one field with a strong yet unsatisfied need for automatically extracting instances of various entity classes from texts is the analysis of socio-technical systems (Feldstein in Media in Transition MiT5, 2007;Hampe et al. in Netzwerkanalyse und Netzwerktheorie, 2007;Weil et al. in Proceedings of the 2006 Command and Control Research and Technology Symposium, 2006;Diesner and Carley in XXV Sunbelt Social Network Conference, 2005). Traditional as well as non-traditional and customized sets of entity classes and the relationships between them are often specified in ontologies or taxonomies. We present a conditional random fields (CRF)-based approach to distilling a set of entities that are defined in an ontology originating from organization science. CRF, a supervised sequential machine learning technique, facilitates the derivation of relational data from corpora by locating and classifying instances of various entity classes. The classified entities can be used as nodes for the construction of socio-technical networks. We find the outcome sufficiently accurate (82.7 percent accuracy of locating and classifying entities) for future application in the described problem domain. We propose using the presented methodology as a crucial step in the process of advanced modeling and analysis of complex and dynamic networks.

关键词： ontological text coding semantic networks entity extraction supervised machine learning conditional models conditional random fields

来源：评论

学校读者我要写书评

暂无评论

conditional random fields - based approach for real-time building occupancy estimation with multi-sensory networks

引用

AUTOMATION IN CONSTRUCTION 2016年 68卷 128-145页

作者： Zikos, Stylianos Tsolakis, Apostolos Meskos, Dimitrios Tryferidis, Athanasios Tzovaras, Dimitrios Ctr Res & Technol Hellas Inst Informat Technol 6th Km Harilaou ThermiPOB 60361 Thessaloniki 57001 Greece

Automated real-time occupancy monitoring in buildings plays an important role in increasing energy efficiency and provides facility managers with useful information about the usage of different spaces. In this paper, a novel approach is proposed for estimating real-time occupancy in buildings, based on conditional random Field probabilistic models, utilizing data from different sensor types. Three different types of occupancy information are considered: presence/absence, actual number of occupants and occupancy density. The proposed occupancy estimation method has been applied to four spaces with different characteristics in a real-life testbed environment. Experimental results revealed that the proposed method yielded high accuracy for different sensor combinations in all tested configurations regarding the occupancy granularity:and the space type, and outperformed the Hidden Markov Model based method. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Building occupancy estimation conditional random fields Multi-sensorial cloud Occupancy sensors

来源：评论

学校读者我要写书评

暂无评论

conditional random fields as message passing mechanism in anchor-free network for multi-scale pedestrian detection

引用

INFORMATION SCIENCES 2021年 550卷 1-12页

作者： Li, Qiming Qiang, Hua Li, Jun Chinese Acad Sci Quanzhou Inst Equipment Mfg Haixi Inst Quanzhou 362216 Fujian Peoples R China

Many deep learning detectors have been proposed to address the scale variation issue in pedestrian detection. However, it is still far from being fully solved because most of these detectors handle the issue by directly fusing multiple extracted features with the weighted average or concatenation. Therefore, this paper designs a novel and effective anchor-free network (namely MPAF-Net) for multi-scale pedestrian detection. Specifically, we first improve Res2Net as our backbone network by designing a novel bottleneck block to make the proposed detector has a strong multi-scale representation ability for pedestrians. Furthermore, based on conditional random fields (CRFs), the multi-scale features obtained from three different feature pyramid levels of backbone network are refined with a designed message passing mechanism. By doing so, the complementary information from the features at other scales can be dynamically passed to enhance the scale-specific feature. Finally, pedestrian detection is simplified as a straightforward center and scale prediction task by performing convolution on the concatenation of refined multi-scale convolution features. This way, the proposed MPAF-Net can be trained in an end-to-end fashion. Experimental results show that MPAF-Net achieves state-of-the-art performance on CityPersons, Caltech, and CrowdHuman benchmarks. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Multi-scale pedestrian detection Anchor-free conditional random fields Message passing mechanism

来源：评论

学校读者我要写书评

暂无评论

conditional random fields for Pattern Recognition Applied to Structured Data

引用

ALGORITHMS 2015年第3期8卷 466-483页

作者： Burr, Tom Skurikhin, Alexei Los Alamos Natl Lab Stat Sci POB 1663 Los Alamos NM 87544 USA Los Alamos Natl Lab Space Data Syst Los Alamos NM 87544 USA

Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is manmade (such as a building) or natural (such as a tree). Suppose the label for a pixel patch is manmade;if the label for a nearby pixel patch is then more likely to be manmade there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.

关键词： conditional random fields image analysis pattern recognition

来源：评论

学校读者我要写书评

暂无评论

conditional random fields FOR OBJECT AND BACKGROUND ESTIMATION IN FLUORESCENCE VIDEO-MICROSCOPY

CONDITIONAL RANDOM FIELDS FOR OBJECT AND BACKGROUND ESTIMATI...

引用

IEEE International Symposium on Biomedical Imaging - From Nano to Macro

作者： Pecot, T. Chessel, A. Bardin, S. Salamero, J. Bouthemy, P. Kervrann, C. Ctr Rennes INRIA F-35042 Bretagne Atlantique France INRA Math Informat Appliquees UR 341 F-78352 Jouy En Josas France IBISA Inst Curie Cell & Tissue Imaging Facil F-75248 Paris France CNRS Inst Curie UMR 144 F-75248 Paris France

ISBN: (纸本)9781424439317

This paper describes an original method to detect XFP-tagged proteins in time-lapse microscopy. Non-local measurements able to capture spatial intensity variations are incorporated within a conditional random Field (CRF) framework to localize the objects of interest. The minimization of the related energy is performed by a min-cut/max-flow algorithm. Furthermore, we estimate the slowly varying background at each time step. The difference between the current image and the estimated background provides new and reliable measurements for object detection. Experimental results on simulated and real data demonstrate the performance of the proposed method.

关键词： Object detection fluorescence biomedical microscopy conditional random fields min-cut/max-flow minimization

来源：评论

学校读者我要写书评

暂无评论

conditional random fields for Spanish Named Entity Recognition Using Unsupervised Features 1

引用

15th Ibero-American Conference on Artificial Intelligence (AI)

作者： Copara, Jenny Ochoa, Jose Thorne, Camilo Glavas, Goran Univ Catolica San Pablo Arequipa Peru Univ Mannheim Data & Web Sci Grp Mannheim Germany

ISBN: (数字)9783319479552

ISBN: (纸本)9783319479552;9783319479545

Unsupervised features based on word representations such as word embeddings and word collocations have shown to significantly improve supervised NER for English. In this work we investigate whether such unsupervised features can also boost supervised NER in Spanish. To do so, we use word representations and collocations as additional features in a linear chain conditional random Field (CRF) classifier. Experimental results (82.44% F-score on the CoNLL-2002 corpus) show that our approach is comparable to some state-of-art Deep Learning approaches for Spanish, in particular when using cross-lingual word representations.

关键词： NER for Spanish Word representations Collocations conditional random fields

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：