Multi-Instance Ensemble Learning With Discriminative Bags

Yang, Mei; Zhang, Yuxuan; Wang, Xizhao; Min, Fan

doi:10.1109/tsmc.2021.3125040

Cited by 17 publications

(6 citation statements)

References 30 publications

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“…In general, each component has a promoting effect on the accuracy of the model, and their cooperation makes the accuracy of the model significantly improved. The analysis of obtaining the gains may be as follows: (a) the grouping strategy in the BAGS module improves the model’s detection ability to defects in tail classes, so as to the overall detection accuracy is improved significantly; 42 (b) the ranking strategy based on anchor sample importance in the ISR module increases the model’s attention to important anchor samples and the important anchor samples are essential for the performance improvement of the model. 28…”

Section: Experiments and Resultsmentioning

confidence: 99%

FocusDet: towards high-quality digital printing fabric defect detection

Su,

Lu,

et al. 2023

Textile Research Journal

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Deep-learning models have been effectively applied to the fabric defect detection field, in which dilemmas still exist for further improving product quality. For the self-built digital printing fabric defect detection dataset, the dilemmas can be expressed in aspects. First, the existing detection models are more inclined to learn many shot categories (head classes) and directly ignore low shot categories (tail classes); Second, the sampled positive and negative anchors in each mini-batch are not equally important, therefore they should be unequally attended to according to their importance. To solve these problems, in this article, a high-quality model for digital printing fabric defect detection was proposed, termed FocusDet. Specially, we construct the model based on the Faster-RCNN framework with two well-designed modules: the balanced group softmax module and the importance-based sample reweighting module, which improve the detection accuracy. Experimental results demonstrate that our proposed model reaches state-of-the-art accuracy on COCO metrics compared with other advanced detection models in the digital printing fabric defect detection dataset.

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Section: Experiments and Resultsmentioning

confidence: 99%

FocusDet: towards high-quality digital printing fabric defect detection

Su,

Lu,

et al. 2023

Textile Research Journal

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“…K-means and SVM [56] 83% Cascaded Deep Learning and Random Forests [57] 77.2% ANN [58] 75.9% Feed Forward Neural Network [59] 74.6% Extreme Learning Machine [60] 81.8% Faster-RCNN [61] 71.2% CNN-based Framework [50] 85.2% Attention [8] 86.2% Gated Attention [8] 86.4% mi-Net Attention [52] 86.7% ELDB [53] 85.8% TGA-MIL (ours)…”

Section: Algorithms Sensitivitymentioning

confidence: 99%

Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis

Thiyagalingam

et al. 2023

Appl Intell

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In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a “bag” as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information.

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“…StableMIL [23] builds upon identifying a novel connection between MIL and the potential outcome framework in causal effect estimation. ELDB [18] introduces discriminative analysis and selfreinforcement mechanisms under the concept of continual learning to maximize the designed discriminative optimization goal. Some others also include PL [11], AEMI [20], MIHI [19] and so on.…”

Section: Related Workmentioning

confidence: 99%

“…Attention-net [6] and loss-attention [10] are two popular MIL networks with well-known attention mechanisms; 3. ELDB [18] designs the discriminative analysis and self-reinforcement mechanisms to optimize the distinguishability of bags' embedding vectors. 4.…”

Section: Comparative Algorithmsmentioning

confidence: 99%

“…A label is assigned to the bag, but not to the individual instances. To date, MIL has also been frequently utilized in a variety of applications, such as image classification [18,19], text categorization [9,17], sentiment analysis [1], web index recommendation [12,16], whole slide images [8,21], and video anomaly detection [7]. Among them, the embedding-based approaches are one of the representative researches in MIL, with the primary notion of transforming bags into a new feature vector and establishing the learning process using SIL methods [17,22].…”

Section: Introductionmentioning

confidence: 99%

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Multi-instance embedding learning with deconfounded instance-level prediction

Zhang

Yang

Zhou

et al. 2022

Int J Data Sci Anal

Self Cite

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Confounded information is an objective fact when using multiple instance learning (MIL) to classify bags of instances, which may be inherited by MIL embedding methods and lead to questionable bag label prediction. To respond to this problem, we propose the multi-instance embedding learning with deconfounded instance-level prediction (MEDI) algorithm. Unlike traditional embedding-based strategies, we design a deconfounded optimization goal to maximize the distinction between instances in positive and negative bags. In addition, we present and use bag-level embedding with feature distillation to reduce the MIL classification task to a single-instance learning problem. Extensive experiments on thirty-four data sets demonstrate that our proposed method has the best overall performance over other state-of-the-art MIL methods. This strategy, in particular, has a substantial advantage on web data sets. Source codes are available at https://github.com/InkiInki/MEDI.

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Multi-Instance Ensemble Learning With Discriminative Bags

Cited by 17 publications

References 30 publications

FocusDet: towards high-quality digital printing fabric defect detection

FocusDet: towards high-quality digital printing fabric defect detection

Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis

Multi-instance embedding learning with deconfounded instance-level prediction

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