检索结果-内蒙古大学图书馆

Abductive multi-instance multi-label learning for periodontal disease classification with prior domain knowledge

MEDICAL IMAGE ANALYSIS 2025年 101卷 103452页

作者： Wu, Zi-Yuan Guo, Wei Zhou, Wei Ye, Han-Jia Jiang, Yuan Li, Houxuan Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing 210023 Peoples R China Nanjing Univ Affiliated Hosp Nanjing Stomatol Hosp Med SchRes Inst Stomatol Nanjing 210008 Peoples R China

Machine learning is widely used in dentistry nowadays, offering efficient solutions for diagnosing dental diseases, such as periodontitis and gingivitis. Most existing methods for diagnosing periodontal diseases follow a two-stage process. Initially, they detect and classify potential Regions of Interest (ROIs) and subsequently determine the labels of the whole images. However, unlike the recognition of natural images, the diagnosis of periodontal diseases relies significantly on pinpointing specific affected regions, which requires professional expertise that is not fully captured by existing models. To bridge this gap, we propose a novel AB ductive M ulti- I nstance M ulti-label learning ( AB-MIML ) approach. In our approach, we treat entire intraoral images as "bags"and local patches as "instances". By improving current multi-instance multi-label methods, AB-MIML seeks to establish a comprehensive many-to-many relationship to model the intricate correspondence among images, patches, and corresponding labels. Moreover, to harness the power of prior domain knowledge, AB-MIML converts the expertise of doctors and the structural information of images into a knowledge base and performs abductive reasoning to assist the classification and diagnosis process. Experiments unequivocally confirm the superior performance of our proposed method in diagnosing periodontal diseases compared to state-of-theart approaches across various metrics. Moreover, our method proves invaluable in identifying critical areas correlated with the diagnosis process, aligning closely with determinations made by human doctors.

关键词： Periodontal disease Abductive learning multi-instance multi-label learning Prior domain knowledge

来源：评论

学校读者我要写书评

暂无评论

Imbalanced multi-instance multi-label learning via tensor product-based semantic fusion

引用

FRONTIERS OF COMPUTER SCIENCE 2025年第8期19卷 1-12页

作者： Zhang, Xinyue Luo, Tingjin Natl Univ Def Technol Coll Sci Changsha 410073 Peoples R China

With powerful expressiveness of multi-instance multi-label learning (MIML) for objects with multiple semantics and its great flexibility for complex object structures, MIML has been widely applied to various applications. In practical MML tasks, the naturally skewed label distribution and label interdependence bring up the label imbalance issue and decrease model performance, which is rarely studied. To solve these problems, we propose an imbalanced multi-instance multi-label learning method via tensor product-based semantic fusion (IMIML-TPSF) to deal with label interdependence and label distribution imbalance simultaneously. Specifically, to reduce the effect of label interdependence, it models similarity between the query object and object sets of different label classes for similarity-structural features. To alleviate disturbance caused by the imbalanced label distribution, it establishes the ensemble model for imbalanced distribution features. Subsequently, IMIML-TPSF fuses two types of features by tensor product and generates the new feature vector, which can preserve the original and interactive feature information for each bag. Based on such features with rich semantics, it trains the robust generalized linear classification model and further captures label interdependence. Extensive experimental results on several datasets validate the effectiveness of IMIML-TPSF against state-of-the-art methods.

关键词： multi-instance multi-label learning tensor product fusion similarity-based learning imbalanced learning feature mapping

来源：评论

学校读者我要写书评

暂无评论

multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation

引用

INFORMATION SCIENCES 2012年 190卷 162-177页

作者： He, Jianjun Gu, Hong Wang, Zhelong Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China

Classification problems have been frequently encountered in visual mobile robot navigation. The studies reported so far are mainly focused on the single label problem;i.e., each sample (datum) is assigned to a single class. In the case when a sample belongs to multiple classes simultaneously, most existing approaches attempt to avoid handling this situation by labeling the samples subjectively with the base class, which is the most obvious to them, or by considering them as a new class. In this paper, a new multi-instance multi-label learning (MIML) algorithm, called MIMLGP, is proposed by using Gaussian process (GP) for solving the multiple labels problems in visual mobile robot navigation. Compared with the existing multi-label (ML) algorithms, the MIMLGP method represents each sample with multiple instances so that higher accuracy may be achieved. Moreover, correlations between the labels associated to the same sample, which are crucial to multi-label learning but rarely considered before, are analyzed by using a covariance matrix present in MIMLGP. Experiments in the area of place recognition and terrain classification have been conducted to substantiate the proposed algorithm. The experimental results show that the proposed algorithm can achieve better performance than the one produced by the existing algorithms. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Gaussian process multi-instance multi-label learning Place recognition Terrain classification Visual navigation

来源：评论

学校读者我要写书评

暂无评论

multi-instance multi-label learning

引用

ARTIFICIAL INTELLIGENCE 2012年第1期176卷 2291-2320页

作者： Zhou, Zhi-Hua Zhang, Min-Ling Huang, Sheng-Jun Li, Yu-Feng Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210046 Jiangsu Peoples R China

In this paper, we propose the MIML (multi-instance multi-label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MIMLBOOST and MIMLSVM algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MIMLSVM algorithm which tackles MIMI, problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the INSDIF and SUBCOD algorithms. INSDIF works by transforming single-instances into the MIML representation for learning, while SUBCOD works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Machine learning multi-instance multi-label learning MIML multi-label learning multi-instance learning

来源：评论

学校读者我要写书评

暂无评论

multi-instance multi-label learning Based on Parallel Attention and Local label Manifold Correlation 9

Multi-instance Multi-label Learning Based on Parallel Attent...

引用

9th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

作者： Yang, Mei Tang, Wen-Tao Min, Fan Southwest Petr Univ Sch Comp Sci Chengdu Peoples R China Southwest Petr Univ Sch Comp Sci Inst Artificial Intelligence Chengdu Peoples R China

ISBN: (纸本)9781665473309

Real-world applications always contain complex data objects with multiple semantics. The machine learning tasks generalized from these applications can be formulated as multiinstance multi-label learning (MIML) problems. Each object is represented by a bag of multiple instances and can be associated with multiple labels simultaneously. Extensive studies on the MIML have been conducted during the past few years. However, many of them do not consider local label correlations, which is critical in multi-label scenario. To address this problem, we propose the multi-instance multi-label learning based on parallel attention and local label manifold correlation (MIML-LLMC) algorithm. First, parallel multiple attention mechanisms convert each bag into a number of fusion vectors, one for each label. They are also utilized to find out which instances in the bag trigger respective labels to improve explanability. Second, local label manifold correlation vectors (LLMCs) are constructed based on label manifold generation and clustering. They provide a high level of representation of the label vectors. Third, each fusion vector is concatenated to the LLMC to predict each label independently. With the utilization of attention and LLMCs, MIML-LLMC is able to discover instance-label relations and exploit local label correlations simultaneously. Experiments reveal that our approach is highly competitive to the state-of-the-art MIML algorithms and yields reasonable results in understanding the relations between input patterns and output label semantics.

关键词： Attention multi-instance multi-label learning Local label correlation label manifold

来源：评论

学校读者我要写书评

暂无评论

multi-instance multi-label learning for Image Categorization Based on Integrated Contextual Information 9th

Multi-instance Multi-label Learning for Image Categorization...

引用

9th International Conference on Image and Graphics (ICIG)

作者： Li, Xingyue Wan, Shouhong Zou, Chang Yin, Bangjie Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

ISBN: (纸本)9783319716077;9783319716060

In image categorization, one image is usually reshaped as a bag of instances affiliated with multiple labels, which naturally induces a paradigm of multi-instance and multi-label learning (MIMLL). Previous researches proved that the significant improvements on image categorization accuracy resulted from applying connections between labels and regions or correlations among labels, but most proposed approaches could not make full use of contextual cues. Thus we propose a brand-new MIMLL method with integrating three distinct types of contexts into a conditional random fields (CRFs) framework, which can simultaneously capture latent probability distribution of instances, spatial context among adjacent instances and correlations between instances and labels. We perform image categorization on MSRC and Corel 1000 data sets to verify our proposal. Compared with traditional and state-of-the-art MIMLL algorithms, our approach obtains the superior performance.

关键词： multi-instance multi-label learning Image categorization Conditional random fields Contextual information

来源：评论

学校读者我要写书评

暂无评论

multi-instance multi-label learning for whole slide breast histopathology 4

Multi-instance multi-label learning for whole slide breast h...

引用

Conference on Medical Imaging - Digital Pathology

作者： Mercan, Caner Mercan, Ezgi Aksoy, Selim Shapiro, Linda G. Weaver, Donald L. Elmore, Joann G. Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkey Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Univ Vermont Dept Pathol Burlington VT 05405 USA Univ Washington Dept Med Seattle WA 98195 USA

ISBN: (纸本)9781510600263

Digitization of full biopsy slides using the whole slide imaging technology has provided new opportunities for understanding the diagnostic process of pathologists and developing more accurate computer aided diagnosis systems. However, the whole slide images also provide two new challenges to image analysis algorithms. The first one is the need for simultaneous localization and classification of malignant areas in these large images, as different parts of the image may have different levels of diagnostic relevance. The second challenge is the uncertainty regarding the correspondence between the particular image areas and the diagnostic labels typically provided by the pathologists at the slide level. In this paper, we exploit a data set that consists of recorded actions of pathologists while they were interpreting whole slide images of breast biopsies to find solutions to these challenges. First, we extract candidate regions of interest (ROI) from the logs of pathologists' image screenings based on different actions corresponding to zoom events, panning motions, and fixations. Then, we model these ROIs using color and texture features. Next, we represent each slide as a bag of instances corresponding to the collection of candidate ROIs and a set of slide-level labels extracted from the forms that the pathologists filled out according to what they saw during their screenings. Finally, we build classifiers using five different multi instance multi-label learning algorithms, and evaluate their performances under different learning and validation scenarios involving various combinations of data from three expert pathologists. Experiments that compared the slide-level predictions of the classifiers with the reference data showed average precision values up to 62% when the training and validation data came from the same individual pathologist's viewing logs, and an average precision of 64% was obtained when the candidate ROIs and the labels from all pathologists were comb

关键词： Digital pathology computer aided diagnosis breast histopathology whole slide imaging region of interest analysis image classification multi-instance multi-label learning

来源：评论

学校读者我要写书评

暂无评论

A New multi-instance multi-label learning approach for image and text classification

引用

multiMEDIA TOOLS AND APPLICATIONS 2016年第13期75卷 7875-7890页

作者： Yan, Kaobi Li, Zhixin Zhang, Canlong Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Guangxi Expt Ctr Informat Sci Guilin 541004 Peoples R China

Recently, a reasonable and effectively framework to deal with the classification problem of the polysemy object with complex connotation is multi-instance multi-label (MIML) learning framework in which each example is not only represented by multiple instances but also associated with multiple labels. As we all know, feature expression plays an important role in the classification problems. It determines the accuracy of the classification results from the source. Considering its difficulties for automatically extracting the high-level features which are useful and noiseless for the MIML problem, so in this paper we present a general MIML framework by combining the feature learning technologies with machine learning technologies. Further, based on this framework, a new approach called CPNMIML which combines the probabilistic latent semantic analysis (PLSA) with the neural networks (NN) is proposed. In CPNMIML algorithm, we firstly learn the latent topic allocation of all the training examples by using the PLSA model, it is a feature learning process to get high-level features. Then we utilize the learned latent topic allocation of each training example to train the neural networks. Given a test example, we learn its latent topic distribution. Finally, we send the learned latent topic allocation of the test example to the trained neural networks to get the multiple labels of the test example. Experiments show that the proposed method has superior performance on two real-world MIML tasks.

关键词： Feature learning multi-instance multi-label learning Probabilistic latent semantic analysis Neural networks Scene classification Text categorization

来源：评论

学校读者我要写书评

暂无评论

A multi-instance multi-label learning algorithm based on instance correlations

引用

multiMEDIA TOOLS AND APPLICATIONS 2016年第19期75卷 12263-12284页

作者： Liu, Chanjuan Chen, Tongtong Ding, Xinmiao Zou, Hailin Tong, Yan Ludong Univ Sch Informat & Elect Engn Yantai 264025 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Shandong Inst Business & Technol Sch Informat & Elect Engn Yantai 264005 Peoples R China

Existing multi-instance multi-label learning algorithms generally assume that instances in a bag are independent of each other, which is difficult to be guaranteed in practical applications. A novel multi-instance multi-label learning algorithm is proposed by modeling instance correlations in each bag. First, instance correlations are introduced in multi-instance multi-label learning by constructing graphs. Then, different kernel matrices are derived from kernel functions based on graphs at different scales, which are employed to train multiple Kernel Support Vector Machine (MKSVM) classifiers. Experimental results on different datasets show that the proposed method significantly improves the accuracy of the multi-label classification compared with the state-of-the-art methods.

关键词： multi-instance multi-label learning multi-label classification instance correlations multiple-kernel fusion

来源：评论

学校读者我要写书评

暂无评论

MIML-GAN: A GAN-Based Algorithm for multi-instance multi-label learning on Overlapping Signal Waveform Recognition

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 859-872页

作者： Pan, Zesi Wang, Bo Zhang, Ruibin Wang, Shafei Li, Yunjie Li, Yan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

Existing studies for automatic waveform recognition of overlapping signals have mostly been conducted in a supervised manner. Although demonstrating superior performance in recent years, supervised methods rely heavily on sufficient labeled samples, but the acquisition of annotated data is expensive, time-consuming, and sometimes infeasible. This shortage drives the need for semi-supervised learning methods, where the unlabeled samples can be fully exploited in the training stage. In addition, multi-instance multi-label (MIML) learning is essentially another weakly supervised learning protocol, and precisely fits the form of the time-frequency images TFIs obtained from the transformation of overlapping signals. In this paper, delving into the MIML learning problem, we leverage the advantage of adversarial training to formulate an effective algorithm MIML-GAN, which is tailored to the MIML problem of overlapping signal waveform recognition. After feeding the TFIs into the network, MIML-GAN approximates the distribution of the training data using the adversarial learning principle. Subsequently, the bag-level prediction can be derived from the instance-level prediction upon the MIML discriminator through adaptive threshold calibration. Specifically, we elaborately studied the global optimality of the MIML-GAN objective function, and extensive simulations are carried out with overlapping signal dataset, validating the ascendancy of the proposed method. Comparative experiments demonstrate that the proposed algorithm possesses promising feature representation capability, and outperforms the existing semi-supervised and supervised signal waveform recognition approaches.

关键词： Task analysis Training Supervised learning Signal processing algorithms Generators Generative adversarial networks Feature extraction Automatic waveform recognition overlapping radar signals residual attention learning generative adversarial network multi-instance multi-label learning

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：