Multi-Label Active Learning Algorithms for Image Classification

Wu, Jian; Sheng, Victor S.; Zhang, Jing; Li, Hua; Dadakova, Tetiana; Swisher, Christine Leon; Cui, Zhiming; Zhao, Pengpeng

doi:10.1145/3379504

Cited by 70 publications

(31 citation statements)

References 83 publications

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“…Glioma is the most common primary brain tumor which originates from glial cells. Because of its characteristic of infiltrating the surrounding tissues, it is difficult to be completely removed by surgery [1]. According to its degree of malignancy, it can be further divided into low-grade gliomas (LGG) and high-grade gliomas (HGG).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Zhang

Zhou

Liao

et al. 2021

Journal of Healthcare Engineering

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In order to improve the accuracy of glioma segmentation, a multimodal MRI glioma segmentation algorithm based on superpixels is proposed. Aiming at the current unsupervised feature extraction methods in MRI brain tumor segmentation that cannot adapt to the differences in brain tumor images, an MRI brain tumor segmentation method based on multimodal 3D convolutional neural networks (CNNs) feature extraction is proposed. First, the multimodal MRI is oversegmented into a series of superpixels that are uniform, compact, and exactly fit the image boundary. Then, a dynamic region merging algorithm based on sequential probability ratio hypothesis testing is applied to gradually merge the generated superpixels to form dozens of statistically significant regions. Finally, these regions are postprocessed to obtain the segmentation results of each organization of GBM. Combine 2D multimodal MRI images into 3D original features and extract features through 3D-CNNs, which is more conducive to extracting the difference information between the modalities, removing redundant interference information between the modalities, and reducing the original features at the same time. The size of the neighborhood can adapt to the difference of tumor size in different image layers of the same patient and further improve the segmentation accuracy of MRI brain tumors. The experimental results prove that it can adapt to the differences and variability between the modalities of different patients to improve the segmentation accuracy of brain tumors.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Zhang

Zhou

Liao

et al. 2021

Journal of Healthcare Engineering

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“…ML also relies on model accuracy when it comes to classification and clustering. DM emphasizes the reliability and scalability of extraction techniques on massive data collections and methods used in structured data and the development of new and alternative methods [135,136].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Seyfollahi

Ghaffari

2021

Wireless Communications and Mobile Computing

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IPv6 routing protocol for low-power and lossy networks (RPL) has been developed as a routing agent in low-power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low-power, high-throughput IPv6 routing protocol that uses distance vectors. Each sensor-to-wire network router has a collection of fixed parents and a preferred parent on the path to the Destination-oriented directed acyclic graph (DODAG) graph’s root in steady-state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.

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“…Active learning is already studied and used in many fields, VOLUME 4, 2016 such as image processing [11]- [14], text processing [15]- [18] and so on [19], [20], while the research of active learning in semi-supervised clustering based on pairwise constraints is relatively limited [21]. In general, existing methods of selecting pairwise constraints can be roughly divided into two categories: initial selection [9], [22] and iterative selection [21].…”

Section: Introductionmentioning

confidence: 99%

An Active Learning Algorithm Based on Shannon Entropy for Constraint-Based Clustering

Chen

Jin

2020

IEEE Access

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Pairwise constraints could enhance clustering performance in constraint-based clustering problems, especially when these pairwise constraints are informative. In this paper, a novel active learning pairwise constraint formulation algorithm would be constructed with aim to formulate informative pairwise constraints efficiently and economically. This algorithm consists of three phases: Selecting, Exploring and Consolidating. In Selecting phase, some type of unsupervised clustering algorithm is used to obtain an informative data set in terms of Shannon entropy. In Exploring phase, some type of farthest-first strategy is used to construct a series of query with aim to construct clustering skeleton set structure and informative pairwise constraints are also collected meanwhile based on the informative data set. If the number of skeleton sets equals the number of clusters, the new algorithm gets into third phase Consolidating; otherwise, it would finish. In Consolidating phase, non-skeleton points included in the informative data set are used to construct a series of query with skeleton set representative points constructed in Exploring phase. And some type of priority principle is proposed to help collect more must-link pairwise constraints. Treat the well-known MPCK-means (metric pairwise constrained K-means) as the underlying constraint-based semi-supervised clustering algorithm and data experiment comparison between this new algorithm and its counterparts would be done. Experiment outcome shows that significant improvement of this new algorithm.

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Multi-Label Active Learning Algorithms for Image Classification

Cited by 70 publications

References 83 publications

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

An Active Learning Algorithm Based on Shannon Entropy for Constraint-Based Clustering

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