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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

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

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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

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

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

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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

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

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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

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Hierarchical multi-instance multi-label learning for Chinese patent text classification

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CONNECTION SCIENCE 2024年第1期36卷

作者： Liu, Yunduo Xu, Fang Zhao, Yushan Ma, Zichen Wang, Tengke Zhang, Shunxiang Tian, Yuhao Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Anhui Univ Sci & Technol Sch Foreign Languages Huainan Peoples R China Anhui Univ Sci & Technol Sch Math & Big Data Huainan Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

To further enhance the accuracy of the Chinese patent classification, this paper proposes a model, based on the patent structure and takes the patent claim as subjects, with multi-instance multi-label learning as the main method. Firstly, the patent claims are divided into multiple independent texts using the sequence number as the splitting token. For each patent, multiple claims are regarded as multiple instances, and the corresponding IPCs serve as its multiple labels. Next, the concept of secondary_label is introduced following the composition rules of IPC, and the relationships between instances and multiple secondary_labels are mined through the construction of fully-connected layers. To capture more comprehensive semantic information of instances, BIGRU and self-attention are employed to enhance semantics and reduce information loss during the training process. Finally, the max-pooling operations are utilised to obtain the predicted categories of patents based on capturing the relationships between instances and different hierarchical labels. Experimental results on the '2017 Chinese patent dataset' demonstrate that the multi-instance multi-label approach can effectively mine deeper relationships between patents and labels in classification tasks. As a result, our model significantly improves the accuracy of patent text classification.

关键词： Patent text classification IPC secondary_label multi-instance multi-label learning patent claim

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Predicting the function of rice proteins through multi-instance multi-label learning based on multiple features fusion

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BRIEFINGS IN BIOINFORMATICS 2022年第3期23卷 bbac095-bbac095页

作者： Liu, Jing Tang, Xinghua Cui, Shuanglong Guan, Xiao Shanghai Maritime Univ Informat Engn Coll Shanghai 201306 Peoples R China Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China

There are a large number of unannotated proteins with unknown functions in rice, which are difficult to be verified by biological experiments. Therefore, computational method is one of the mainstream methods for rice proteins function prediction. Two representative rice proteins, indica protein and japonica protein, are selected as the experimental dataset. In this paper, two feature extraction methods (the residue couple model method and the pseudo amino acid composition method) and the Principal Component Analysis method are combined to design protein descriptive features. Moreover, based on the state-of-the-art MIML algorithm EnMIMLNN, a novel MIML learning framework MK-EnMIMLNN is proposed. And the MK-EnMIMLNN algorithm is designed by learning multiple kernel fusion function neural network. The experimental results show that the hybrid feature extraction method is better than the single feature extraction method. More importantly, the MK-EnMIMLNN algorithm is superior to most classic MIML learning algorithms, which proves the effectiveness of the MK-EnMIMLNN algorithm in rice proteins function prediction.

关键词： rice protein function prediction machine learning multi-instance multi-label learning feature extraction kernel function

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Automatic Waveform Recognition of Overlapping LPI Radar Signals Based on multi-instance multi-label learning

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1275-1279页

作者： Pan, Zesi Wang, Shafei Zhu, Mengtao Li, Yunjie Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Northern Inst Elect Equipment Beijing 100091 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

In an ever-increasingly complex electromagnetic environment, multiple low probability of intercept (LPI) radar emitters may transmit their own signals simultaneously on similar bands, resulting in overlapping receiving signals in both time and frequency domain. In this letter, a novel multi-instance multi-label learning framework based on Deep Convolutional Neural Network (MIML-DCNN) is proposed to automatically recognize the overlapping LPI radar signals,which is trained by single type of signals only. The framework handles signals in an end-to-end manner that is integrated with a well-designed instance generation module, a sophisticated MIML classifier, and an adaptive threshold calibration. Through comprehensive experiments on simulated overlapping signals with four different modulation types, we prove that the proposed framework identifies each individual signal type precisely in the presence of overlapping signals, and is also robust to variation of the signal-to-noise ratio (SNR) and power ratio conditions.

关键词： Radar Modulation Signal to noise ratio Feature extraction Robustness Task analysis Training LPI radar signals overlapping signal recognition multi-instance multi-label learning deep convolutional neural network

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Fast multi-instance multi-label learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019年第11期41卷 2614-2627页

作者： Huang, Sheng-Jun Gao, Wei Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications;however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics.

关键词： multi-instance multi-label learning fast key instance sub-concepts

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DEEP FEATURE REPRESENTATIONS AND multi-instance multi-label learning OF WHOLE SLIDE BREAST HISTOPATHOLOGY IMAGES

DEEP FEATURE REPRESENTATIONS AND MULTI-INSTANCE MULTI-LABEL ...

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作者： Caner Mercan Bilkent University

学位级别：博士

The examination of a tissue sample has traditionally involved a pathologist investigating the case under a microscope. Whole slide imaging technology has recently been utilized for the digitization of biopsy slides, replicating the microscopic examination procedure with the computer screen. This technology made it possible to scan the slides at very high resolutions, reaching up to 100, 000 × 100, 000 pixels. The advancements in the imaging technology has allowed the development of automated tools that could help reduce the workload of pathologists during the diagnostic process by performing analysis on the whole slide histopathology images. One of the challenges of whole slide image analysis is the ambiguity of the correspondence between the diagnostically relevant regions in a slide and the slide-level diagnostic labels in the pathology forms provided by the pathologists. Another challenge is the lack of feature representation methods for the variable number of variable-sized regions of interest (ROIs) in breast histopathology images as the state-of-the-art deep convolutional networks can only operate on fixed-sized small patches which may cause structural and contextual information loss. The last and arguably the most important challenge involves the clinical significance of breast histopathology, for the misdiagnosis or the missed diagnoses of a case may lead to unnecessary surgery, radiation or hormonal therapy. We address these challenges with the following contributions. The first contribution introduces the formulation of the whole slide breast histopathology image analysis problem as a multi-instance multi-label learning (MIMLL) task where a slide corresponds to a bag that is associated with the slide-level diagnoses provided by the pathologists, and the ROIs inside the slide correspond to the instances in the bag. The second contribution involves a novel feature representation method for the variable number of variable-sized ROIs using the activations of d

关键词： multi-instance multi-label learning deep convolutional features whole slide imaging breast histopathology digital pathology medical image analysis

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An improved multi-instance multi-label learning algorithm based on representative instances selection and label correlations

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2018年第3期9卷 268-277页

作者： Liu, Chanjuan Chen, Tongtong Zou, Hailin Ding, Xinmiao Wang, Yuling Ludong Univ Sch Informat & Elect Engn Prov Key Lab Cyber Phys Syst & Intelligent Contro Yantai 264025 Peoples R China Shandong Inst Business & Technol Sch Informat & Elect Engn Yantai 264005 Peoples R China Shandong Jianzhu Univ Dept Informat & Elect Engn Jinan 250101 Shandong Peoples R China

multi-instance multi-label learning (MIML) has been successfully used in image and text classification problems. It is noteworthy that few of the previous studies consider the pattern-label relations. Inevitably, there are some useless instances in a bag which will reduce the accuracy of the annotation. In this paper we focus on this problem. Firstly, an instance selection method via joint l(2,1)-norms constraint is employed to eliminate the useless instances and select the representative instances by modelling the instance correlation. Then, bags are mapped to these representative instances. Finally, the classifier is trained by an optimisation algorithm based on label correlations. Experimental results on image data set, text data sets and bird song audio data set show that the proposed algorithm significantly improves the performance of MIML classifier compared with the state-of-the-art methods.

关键词： multi-instance multi-label learning representative instances selection joint l(2,1)-norms constraint label correlations

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