Scalable Algorithms for Multi-Instance Learning.

Aiming at the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm based on RS (rough set) attribute reduction and Transductive support vector machine (TSVM) has been presented---RSTSVM- MIL algorithm. This algorithm regards the whole image as a bag, and the low-level visual feature of the segmented regions as instances, in order to transform every bag to a single sample, RSTSVM-MIL first generates a collection of visual-word to construct projection space by K-means clustering method in the instance feature space, then a nonlinear mapping is defined using these visual-word and maps every bag to a point in the projection space, extracting all bags' projection feature, which converts MIL problem to a standard supervised learning problem. Finally, in order to improve the efficiency of classifier training and to use a large amount of unlabeled image together with the labeled image to build better classifiers, we use RS (rough set) method to reduce the redundant information in projection feature firstly, then semi-supervised learning algorithm TSVM be used to train classifier. Experimental results on the SIVAL image set show that solving MIL problems using semi-supervised learning is feasible, and the RSTSVM-MIL is competitive with other state-of-the-art MIL algorithms in object- based image retrieval problem.

In this paper, a novel multi-instance learning (MIL) algorithm based on pyramid match kernel (PMK) and classifier ensemble is proposed for recognizing pornographic scene from image database. First, an improved JSEG image segmentation technique is deployed for dividing every image into several regions, and regards the whole image as a "bag", the low-level visual features (i.e. color and texture) of each segmented region as "instance". As a result, the pornographic images filtering problem can be transferred into a typical MIL problem. Second, similarity between the multi-instance bags is measured by PMK method, which allows MIL problem to be solved directly by the support vector machine (SVM). Finally, many base classifiers based on PMK with different levels are constructed, and the performance weighting rule is used to dynamically determine the weights of them, so the strategy of classifier ensemble is used to improve the filtering accuracy. In a real condition image set that the ratio of normal image to pornographic image is 9:1, experimental results show that the proposed algorithm, named PMKCE-MIL, is robust, and its performance is superior to other algorithms.

In this paper, the multi-instance learning algorithm is improved under the image retrieval framework based on contents, and the improved multi-instance learning algorithm is applied to image retrieval to better handle the ambiguity of the image. In this method, the image is used as the multi-instance bag and is divided into multiple instances by image segmentation algorithm, and then the multi-instance learning is performed with the multi-objective-diverse-density algorithm. The learning results are ordered by image similarity using the vector space model. Finally, relevant feedback is given in accordance with the positive bag and negative bag chosen by the user to provide satisfactory results to the user.

This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. Key to our new approach is the unification of multi-instance learning (MIL) with deep learning. Benefiting from that, our method can directly use off-the-shelf clinical data, which involves no labels to indicate nodule classes. To this end, we take the US images of a patient as a bag, and take the corresponding pathology report as the bag label. Specifically, we first propose a bag generating method, wherein the detected thyroid nodules are considered as instances corresponding to certain bag. After that, we design an effective EM algorithm to train a convolutional neural network (CNN) for nodule classification. We conduct extensive experiments and comprehensive evaluations on different datasets, and all the experiments confirm that, our method significantly outperforms state-of-the-art MIL algorithms, which exhibits great potential in clinical applications.

In multi-instance learning, each example is represented by a bag of instances while associated with a binary label. Under standard multi-instance learning settings, one example is labeled as a positive bag if at least one of its instances is positive. Otherwise, it is labeled as a negative bag. Although based on the above assumption, standard multi-instance learning has achieved much success in solving diverse learning tasks, there are still many real-world problems where this assumption may not necessarily hold. Therefore, researchers aimed to expand the underlying assumption of standard multi-instance learning where two frameworks of generalized multi-instance learning have been proposed. In this paper, the problem definition, learning algorithms and also experimental data sets related to either generalized multi-instance learning framework are briefly reviewed.

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

In this paper, a multi-instance learning framework is introduced to solve the problem of skin biopsy image features recognition. Previously reported methods for skin surface images were mostly based on color features extraction. They are incapable to be directly applied to skin biopsy image features recognition because biopsy images are often dyed and have obvious inner structures with different textures. Therefore, we regard skin biopsy images as multi-instance samples, whose instances are regions or structures captured by applying Normalized Cut. Texture feature extraction methods are used to express each region as a vectorial expression. Then two multi-instance learning algorithms reported successful in various image retrieval tasks were applied. Nine features were manually selected as target features to evaluate the proposed method on a skin disease diagnosis datasets of 6579 biopsy images from 2010 to 2011. The result showed that the proposed method is effective and medically acceptable.

Multi-instance learning (MIL) has received more and more attentions in the machine learning research field due to its theoretical interest and its applicability to diverse real-world problems. In this paper, we present a probabilistic kernel approach for the multi-instance learning problems with various multi-instance assumptions by imposing Gaussian process prior on an unobservable latent function defined on the instance space. Because the relationship between the bag and its instances, triggered by the multi-instance assumption, can be exactly captured by defining the likelihood function, we can deal with different multi-instance assumptions by employing different likelihood functions. Experimental results on several multi-instance problems show that the proposed algorithms are valid and can achieve superior performance to the published MIL algorithms.

In multi-instance learning, the training set is composed of labeled bags each consists of many unlabeled instances, that is, an object is represented by a set of feature vectors instead of only one feature vector. Most current multi-instance learning algorithms work through adapting single-instance learning algorithms to the multi-instance representation, while this paper proposes a new solution which goes at an opposite way, that is, adapting the multi-instance representation to single-instance learning algorithms. In detail, the instances of all the bags are collected together and clustered into d groups first. Each bag is then re-represented by d binary features, where the value of the ith feature is set to one if the concerned bag has instances falling into the ith group and zero otherwise. Thus, each bag is represented by one feature vector so that single-instance classifiers can be used to distinguish different classes of bags. Through repeating the above process with different values of d, many classifiers can be generated and then they can be combined into an ensemble for prediction. Experiments show that the proposed method works well on standard as well as generalized multi-instance problems.

In this paper, we present an automatic web image and video mining framework with the ultimate goal of building a universal human age estimator based on facial information, which is applicable to all ethnic groups and various image qualities. On one hand, a large (391 k) yet noisy human aging image database is collected from Flickr and Google Image using a set of human age-related text queries. Multiple human face detectors based on distinctive techniques are adopted for noise-prune face detection. For each image, the detected faces with high detection confidences constitute a bag of face instances. We further remove the outliers via principal component analysis (PCA), which results in a condensed image database with about 175 k face instances. A robust multi-instance regressor learning algorithm is then developed to learn the kernel regression-based human age estimator in the presence of bag label noises. On the other hand, about 10 k video clips are downloaded from YouTube. We extract tracked face sequences from these video clips. Although their age labels are unknown, the tracked faces within a sequence are naturally with identical ages. This age-consistence constraint for face pairs is used as an extra regularizer to enhance the robustness of the age estimator. The derived human age estimator is extensively evaluated on three benchmark human aging databases, and without taking any images from these benchmark databases as training samples, comparable age estimation accuracies with the state-of-the-art results are achieved.

Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning.

Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study

The biggest organ that is present in the human body, which generally covers all the major body parts.The functions of skin in the human body are more significant, since even a little alteration in one of these functions could have an impact on other body components. Skin is more susceptible to disease and infection because it is exposed to the outside environment. Therefore, we must pay more attention to skin diseases. Lesion areas refer to the affected area of skin. The initial clinical indications of diseases like chickenpox, melanoma, etc. are skin lesions. Today, computer-aided diagnosis is more prevalent in the medical industry. For the automation of many operations in the medical profession, machine learning is crucial. It has been shown that dermoscopy, in the hands of untrained dermatologists, may actually reduce the diagnostic accuracy. There are other illnesses that are equally important in the medical community. Therefore, in addition to the diseases stated above, we also take into account two other diseases, bullae and shingles. The characteristics utilized to characterize the lesion locations are an additional crucial factor. For this, we take into account aspects of color and texture. The learning algorithms are a further topic that needs to be considered. Although there are many classifiers in use, fusion of the results of different classifiers is rapidly gaining prominence since it helps improve categorization. The final outcome the author wants is to use both the algorithms and find a novel accuracy and comapare both of them.

Cross-modal hashing is a highly effective technique for searching relevant data across different modalities, owing to its low storage costs and fast similarity retrieval capability. While significant progress has been achieved in this area, prior investigations predominantly concentrate on a one-to-one feature alignment approach, where a singular feature is derived for similarity retrieval. However, the singular feature in these methods fails to adequately capture the varied multi-instance information inherent in the original data across disparate modalities. Consequently, the conventional one-to-one methodology is plagued by a semantic mismatch issue, as the rigid one-to-one alignment inhibits effective multi-instance matching. To address this issue, we propose a novel Diverse Instances Matching for Cross-modal Hashing (DIMCH), which explores the relevance between multiple instances in different modalities using a multi-instance learning algorithm. Specifically, we design a novel diverse instances learning module to extract a multi-feature set, which enables our model to capture detailed multi-instance semantics. To evaluate the similarity between two multi-feature sets, we adopt the smooth chamfer distance function, which enables our model to incorporate the conventional similarity retrieval structure. Moreover, to sufficiently exploit the supervised information from the semantic label, we adopt the weight cosine triplet loss as the objective function, which incorporates the multilevel similarity among the multi-labels into the training procedure and enables the model to mine the multi-label correlation effectively. Extensive experiments demonstrate that our diverse hashing embedding method achieves state-of-the-art performance in supervised cross-modal hashing retrieval tasks.

Develop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunotherapy (NICT), by leveraging whole slide images (WSIs). This retrospective study examined pre-treatment WSIs from 186 patients with non-small cell lung cancer (NSCLC), using a weakly supervised learning framework. We employed advanced deep learning architectures, including DenseNet121, ResNet50, and Inception V3, to analyze WSIs on both micro (patch) and macro (slide) levels. The training process incorporated innovative data augmentation and normalization techniques to bolster the robustness of the models. We evaluated the performance of these models against traditional clinical predictors and integrated them with a novel pathomics signature, which was developed using multi-instance learning algorithms that facilitate feature aggregation from patch-level probability distributions. Univariate and multivariable analyses confirmed histology as a statistically significant prognostic factor for MPR (P-value< 0.05). In patch model evaluations, DenseNet121 led in the validation set with an area under the curve (AUC) of 0.656, surpassing ResNet50 (AUC = 0.626) and Inception V3 (AUC = 0.654), and showed strong generalization in external testing (AUC = 0.611). Further evaluation through visual inspection of patch-level data integration into WSIs revealed XGBoost's superior class differentiation and generalization, achieving the highest AUCs of 0.998 in training and robust scores of 0.818 in validation and 0.805 in testing. Integrating pathomics features with clinical data into a nomogram yielded AUC of 0.819 in validation and 0.820 in testing, enhancing discriminative accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) and feature aggregation methods notably boosted the model's interpretability and feature modeling. The application of weakly supervised deep learning to WSIs offers a powerful tool for predicting MPR in NSCLC patients treated with NICT.