Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance. [PDF]
PLoS ONE, 2019 We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice
Oduwa Edo-Osagie +4 more
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Subclass-Aware Contrastive Semi-Supervised Learning for Inflammatory Bowel Disease Classification from Colonoscopy Images [PDF]
BioengineeringInflammatory bowel disease (IBD) includes Crohn’s disease (CD) and ulcerative colitis (UC). The accurate classification of IBD from colonoscopy images is critical for diagnosis and treatment.
Kechen Lin +5 more
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An integrated approach for rare disease detection and classification in Spanish pediatric medical reports [PDF]
Scientific ReportsRare disease detection and classification is one of the most significant challenges in the application of Natural Language Processing techniques to the analysis and extraction of information from biomedical texts.
Andres Duque +5 more
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Self-Supervised Assisted Semi-Supervised Residual Network for Hyperspectral Image Classification
Remote Sensing, 2022 Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with this
Liangliang Song +4 more
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LMGAN: Linguistically Informed Semi-Supervised GAN with Multiple Generators
Sensors, 2022 Semi-supervised learning is one of the active research topics these days. There is a trial that solves semi-supervised text classification with a generative adversarial network (GAN).
Whanhee Cho, Yongsuk Choi
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Semi-Supervised Hierarchical Graph Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, or a document in a document citation network.
Jia Li +3 more
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SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification ...
H. Huang, J. Liu, Y. Pan
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A review on graph-based semi-supervised learning methods for hyperspectral image classification
Egyptian Journal of Remote Sensing and Space Sciences, 2020 In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial ...
Shrutika S. Sawant, Manoharan Prabukumar
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Remote Sensing, 2023 Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner.
Chunyu Li, Rong Cai, Junchuan Yu
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SSBTCNet: Semi-Supervised Brain Tumor Classification Network
IEEE Access, 2023 Classification of brain tumors from the Magnetic Resonance Imaging (MRI) is a vital and challenging task for brain tumor diagnosis. Despite favorable results, from current Deep Learning (DL) methods used for the classification of brain tumors, the ...
Zubair Atha, Jyotismita Chaki
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