AIMC Topic: Supervised Machine Learning

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Fuzzy spatiotemporal event-triggered control for the synchronization of IT2 T-S fuzzy CVRDNNs with mini-batch machine learning supervision.

Neural networks : the official journal of the International Neural Network Society Feb 5, 2025
This paper is centered on the development of a fuzzy memory-based spatiotemporal event-triggered mechanism (FMSETM) for the synchronization of the drive-response interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy complex-valued reaction-diffusion neural...
Computer Simulation Fuzzy Logic Supervised Machine Learning Machine Learning Humans Algorithms Neural Networks, Computer
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Fast In Vivo Two-Photon Fluorescence Imaging via Lateral and Axial Resolution Restoration With Self-Supervised Learning.

Journal of biophotonics Feb 5, 2025
Two-photon fluorescence (TPF) imaging opens a new avenue to achieve high resolution at extended penetration depths. However, it is difficult for conventional TPF imaging systems to simultaneously achieve high resolution and speed. In this work, we de...
Microscopy, Fluorescence, Multiphoton Optical Imaging Image Processing, Computer-Assisted Mice Supervised Machine Learning Signal-To-Noise Ratio Deep Learning Time Factors Animals
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Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.

Biomedical engineering online Feb 4, 2025
BACKGROUND: Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodon...
Malocclusion Orthodontics Image Processing, Computer-Assisted Cephalometry Supervised Machine Learning Automation Humans
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DC²T: Disentanglement-Guided Consolidation and Consistency Training for Semi-Supervised Cross-Site Continual Segmentation.

IEEE transactions on medical imaging Feb 4, 2025
Continual Learning (CL) is recognized to be a storage-efficient and privacy-protecting approach for learning from sequentially-arriving medical sites. However, most existing CL methods assume that each site is fully labeled, which is impractical due ...
Humans Image Processing, Computer-Assisted Supervised Machine Learning Algorithms
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A Multi-Perspective Self-Supervised Generative Adversarial Network for FS to FFPE Stain Transfer.

IEEE transactions on medical imaging Feb 4, 2025
In clinical practice, frozen section (FS) images can be utilized to obtain the immediate pathological results of the patients in operation due to their fast production speed. However, compared with the formalin-fixed and paraffin-embedded (FFPE) imag...
Generative Adversarial Networks Humans Frozen Sections Image Processing, Computer-Assisted Paraffin Embedding Staining and Labeling Supervised Machine Learning
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GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation.

IEEE transactions on medical imaging Feb 4, 2025
Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches have been adopted using large...
Humans Algorithms Databases, Factual Supervised Machine Learning Image Interpretation, Computer-Assisted
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Semi-supervised learning for multi-view and non-graph data using Graph Convolutional Networks.

Neural networks : the official journal of the International Neural Network Society Feb 3, 2025
Semi-supervised learning with a graph-based approach has become increasingly popular in machine learning, particularly when dealing with situations where labeling data is a costly process. Graph Convolution Networks (GCNs) have been widely employed i...
Neural Networks, Computer Algorithms Humans Supervised Machine Learning Deep Learning
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Self-Supervised Image Segmentation Using Meta-Learning and Multi-Backbone Feature Fusion.

International journal of neural systems Feb 3, 2025
Few-shot segmentation (FSS) aims to reduce the need for manual annotation, which is both expensive and time-consuming. While FSS enhances model generalization to new concepts with only limited test samples, it still relies on a substantial amount of ...
Humans Neural Networks, Computer Supervised Machine Learning Algorithms Image Processing, Computer-Assisted
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Weakly supervised multi-modal contrastive learning framework for predicting the HER2 scores in breast cancer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Feb 3, 2025
Human epidermal growth factor receptor 2 (HER2) is an important biomarker for prognosis and prediction of treatment response in breast cancer (BC). HER2 scoring is typically evaluated by pathologist microscopic observation on immunohistochemistry (IH...
Breast Neoplasms Supervised Machine Learning Image Interpretation, Computer-Assisted Receptor, ErbB-2 Humans Pathology
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Break Adhesion: Triple adaptive-parsing for weakly supervised instance segmentation.

Neural networks : the official journal of the International Neural Network Society Feb 1, 2025
Weakly supervised instance segmentation (WSIS) aims to identify individual instances from weakly supervised semantic segmentation precisely. Existing WSIS techniques primarily employ a unified, fixed threshold to identify all peaks in semantic maps. ...
Supervised Machine Learning Semantics Neural Networks, Computer Algorithms Humans
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