An ontological bagging approach for image classification of crowdsourced data

Multiple instance learning (MIL) is a new paradigm in machine learning that deals with classification of bags of instances, as opposed to the traditional view that aims at learning from single instances. In a typical MIL setting, a negative bag is composed of only negative instances. On the other hand, a bag is considered positive if it contains at least one positive instance. This learning approach provides a natural way of modeling several pattern recognition and computer vision problems, e.g. protein, document and image classification and object recognition, which inherently require learning under ambiguity. In many cases, MIL approaches performs better than the standard single instance learning (SIL) methods. One of the general approaches to MIL is to transform a given MIL problem into a corresponding SIL problem. This transformation is mainly done with selecting a set of representative instances from the training bags, and these group of studies basically differ from each other on how they perform this instance selection step. In this study, we revisit such a MIL approach called MILIS with a different instance selection mechanism. The experimental results show that the proposed approach performs better on MIL benchmark data sets as compared to the original algorithm.

Multiple instance learning: A survey of problem characteristics and applications

We address the task of automatic detection of lesions caused by multiple myeloma (MM) in femurs or other long bones from CT data. Such detection is already an important part of the multiple myeloma diagnosis and staging. However, it is so far performed mostly manually, which is very time consuming. We formulate the detection as a multiple instance learning (MIL) problem, where instances are grouped into bags and only bag labels are available. In our case, instances are regions in the image and bags correspond to images. This has the advantage of requiring only subject-level annotation (ground truth), which is much easier to get than voxel-level manual segmentation. We consider a generalization of the standard MIL formulation where we introduce a threshold on the number of required positive instances in positive bags. This corresponds better to the classification procedure used by the radiology experts and is more robust with respect to false positive instances. We extend several existing MIL algorithms to solve the generalized case by estimating the threshold during learning. We compare the proposed methods with the baseline method on a dataset of 220 subjects. We show that the generalized MIL formulation outperforms standard MIL methods for this task. For the task of distinguishing between healthy controls and MM patients with infiltrations, our best method makes almost no mistakes with a mean AUC of 0.982 and F1 = 0.965. We outperform the baseline method significantly in all conducted experiments.

Infrared and Visible Image Fusion (IVIF) produces enhanced images by fusing complementary visual information. However, most existing methods generate fixed outputs and cannot flexibly adapt to user-specific requirements. Recent text-guided approaches offer partial control but are limited to global or semantic levels, lacking instance-level control. This limitation arises from two challenges: first, the lack of datasets that directly link textual instructions with corresponding spatial annotations, and second, the use of coarse cross-modal alignment methods that struggle to precisely match textual instructions with visual features. To overcome these challenges, we propose ControlFuse, a controllable IVIF framework enabling multi-granularity fusion across global, semantic, and instance levels, guided by user instructions. First, we construct an automated multi-granularity dataset that provides explicit textual-mask correspondences at these three levels. Second, inspired by manifold geometry, we design a Multimodal Feature Interaction Module (MFIM) comprising Feature Manifold Converter (FMC) and Curvature-Guided Interaction (CGI). FMC projects textual and visual features into a unified manifold space, while CGI leverages manifold curvature as a geometric cue to refine cross-modal alignment. Extensive experiments validate ControlFuse, outperforming state-of-the-art methods in robustness and flexibility.

For the past 2 decades, detection of buried explosive hazard has been studied extensively and several machine learning algorithms have been developed and adapted to this application. First, a pre-screener is used to identify areas of interest or alarms. Each alarm consists of a 3D data cube that corresponds to spatial down-track, crosstrack, and time respectively. Ground truth information is then used to label each alarm and generate labeled data to train a classifier to discriminate between targets and clutter objects. One of the main challenges in this approach is localizing the true depth of the alarm. On one hand, the buried object signature is not expected to cover all the depth values and extracting one global feature from all depth bins may not discriminate between object and clutter signatures effectively. On the other hand, depth ground truth is not available as this depends on the target type and size, soil properties, and other environmental conditions. Moreover, visually inspecting each alarm to select the optimal depth location is tedious, ambiguous, and not practical for very large training data. Two different approaches have been considered to train learning algorithms. The first one uses simple rules and machine learning algorithms to automate the selection of the optimal depth(s) for each alarm. The second approach avoids the labeling at the depth level and uses multiple instance learning (MIL) algorithms. In this context, each alarm is represented by a bag of multiple instances. Each instance corresponds to a feature extracted at a different depth. Since labels are needed at the bag level and not at the instance level, MIL does not require true depth information. In this paper, we propose a large-scale evaluation of the two approaches. For the first approach, we consider three methods to identify optimal depth locations and analyze their effect on the KNN and SVM classifiers. For the second approach, we consider four MIL algorithms that do not require depth information. For our analysis, we use large data collections accumulated across multiple dates and multiple test sites by a vehicle mounted downward looking ground penetrating radar (GPR) sensor. The data include a diverse set of buried explosive objects of varying shapes, metal content, and underground burial depths. Performance of all algorithms is analyzed using receiver operating characteristics (ROC).

Given the special situation of modeling gigapixel images, multiple instance learning (MIL) has become one of the most important frameworks for Whole Slide Image (WSI) classification. In current practice, most MIL networks often face two unavoidable problems in training: i) insufficient WSI data and ii) the sample memorization inclination inherent in neural networks. These problems may hinder MIL models from adequate and efficient training, suppressing the continuous performance promotion of classification models on WSIs. Inspired by the basic idea of Mixup, this paper proposes a new Pseudo-bag Mixup (PseMix) data augmentation scheme to improve the training of MIL models. This scheme generalizes the Mixup strategy for general images to special WSIs via pseudo-bags so as to be applied in MIL-based WSI classification. Cooperated by pseudo-bags, our PseMix fulfills the critical size alignment and semantic alignment in Mixup strategy. Moreover, it is designed as an efficient and decoupled method, neither involving time-consuming operations nor relying on MIL model predictions. Comparative experiments and ablation studies are specially designed to evaluate the effectiveness and advantages of our PseMix. Experimental results show that PseMix could often assist state-of-the-art MIL networks to refresh their classification performance on WSIs. Besides, it could also boost the generalization performance of MIL models in special test scenarios, and promote their robustness to patch occlusion and label noise. Our source code is available at https://github.com/liupei101/PseMix.

The computer aided diagnosis (CAD) problems of detecting potentially diseased structures from medical images are typically distinguished by the following challenging characteristics: extremely unbalanced data between negative and positive classes; stringent real-time requirement of online execution; multiple positive candidates generated for the same malignant structure that are highly correlated and spatially close to each other. To address all these problems, we propose a novel learning formulation to combine cascade classification and multiple instance learning (MIL) in a unified min-max framework, leading to a joint optimization problem which can be converted to a tractable quadratically constrained quadratic program and efficiently solved by block-coordinate optimization algorithms. We apply the proposed approach to the CAD problems of detecting pulmonary embolism and colon cancer from computed tomography images. Experimental results show that our approach significantly reduces the computational cost while yielding comparable detection accuracy to the current state-of-the-art MIL or cascaded classifiers. Although not specifically designed for balanced MIL problems, the proposed method achieves superior performance on balanced MIL benchmark data such as MUSK and image data sets.

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

Rapid improvements in the world of information technology have resulted in a demand for data collecting and the act of storing data for future retrieval. As a result, many businesses and organisations are driven to collect and store massive amounts of data in the data warehouse, a recently built database module. Data classification is a strategy for building a data classifier model that describes the relevant data classes and allows for a better comprehension of the data. It is important to highlight that this data classification approach is not well defined and is a non-deterministic process because there is no guarantee that the data classifier model constructed from past data would work better for future data. In the light of these facts, this chapter discusses the data classification method and its usefulness in the engineering and science fields. The significance and need for a research contribution for data classification using various machine learning models with optimization methods are highlighted, bringing the research work’s aims to light. In this chapter, the various approaches used for designing and developing classifier models, as well as their benefits and drawbacks, are discussed, as well as the hybrid support vector machine with grey wolf optimization (HSVMGWO) used to classify the various datasets. After a thorough evaluation, the proposed HSVMGWO method outperformed the four types of conventional methods: random forest, support vector machine, ada boost algorithm, and finally decision tree, with an efficiency of 95.3 percent, sensitivity of 97.08 percent, specificity of 95.68 percent, and time responsibility of 13.186 M.sec. Statistical study of accuracy values and calculation time shows that the proposed schemes outperform existing approaches.KeywordsGrey wolf optimizationSupport vector machineMultivariate datasetRandom forestAda boost algorithmDecision tree

In multiple instance learning, the training set consists of labeled bags that include unlabeled instances, and the target is to predict the labels of unseen bags. A bag is labeled positive only if it contains at least one positive instance, otherwise it is a negative bag. Over the past years, many popular machine learning algorithms have been adapted to tackle the multiple instance learning problems. In this paper, to train a discriminative multiple instance classifier which generalize well, we present a boosting approach with global smoothness regularization, in which the weak learners are either hyper balls with the center at the instance of positive bags or random projection decision stumps. Experimental results show that our proposed algorithm is comparable to the classical Diverse Density algorithm on some multiple instance learning benchmark datasets.

Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

How artificial intelligence can enable data classification for market sizing - Insights from applications in practice

Objective: Data Mining is one of the majority inspiring areas of research to be develop into more and more accepted in health care organization. Advance structures of classifiers from imbalanced datasets are described. Class imbalance is a vital difficulty in machine learning and occurs in many domains most medical datasets are not balanced in their class labels. Usual classifiers do not carry out well when allowing for data at risk to both within-class and between-class imbalances. Methodology: Most obtainable classification methods tend not to do well on minority class examples when the dataset is very imbalanced. His research paper proposes the result of the accurateness of the result by using the Priority Based Class Clustered under sampling Technique approaches for imbalanced data classification. Findings: In attendance variations of the Adaptive K-means cluster analysis such that the imbalanced nature of the problem is openly addressed in the new algorithm formulation. Improvements: The present paper proposes a cluster-based priority under-sampling approach to select the representative data as training data to get better categorization and correctness for minority class to examine the result of under-sampling methods in the imbalanced class distribution environment. Keywords: Class, wClustering, Data Mining, Imbalanced Data, PBCCUT

Object proposals are a series of candidate segments containing objects of interest, which are taken as preprocessing and widely applied in various vision tasks. However, most of existing saliency approaches only utilize the proposals to compute a location prior. In this paper, we naturally take the proposals as the bags of instances of multiple instance learning (MIL), where the instances are the superpixels contained in the proposals, and formulate saliency detection problem as a MIL task (i.e., predict the labels of instances using the classifier in the MIL framework). This method allows some flexibility in finding a decision boundary based on the bag-level representations and can identify salient superpixels from ambiguous proposals. In addition, we introduce the MIL to an optimization mechanism, which iteratively updates training bags from easy to complex ones to learn a strong model. The significant improvement can be consistently achieved when applying the optimization model to existing saliency approaches. Extensive experiments demonstrate that the proposed algorithms perform favorably against the stateof- art saliency detection methods on several benchmark datasets.

Weakly-supervised learning to automatically count cotton flowers from aerial imagery