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Supervised Machine Learning

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A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity through stimulation or mo...

Predicting the Risk of Maxillary Canine Impaction Based on Maxillary Measurements Using Supervised Machine Learning.

Orthodontics & craniofacial research
OBJECTIVES: To predict palatally impacted maxillary canines based on maxilla measurements through supervised machine learning techniques.

AmpClass: an Antimicrobial Peptide Predictor Based on Supervised Machine Learning.

Anais da Academia Brasileira de Ciencias
In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicin...

Self-adaptive label discovery and multi-view fusion for complementary label learning.

Neural networks : the official journal of the International Neural Network Society
Unlike traditional supervised classification, complementary label learning (CLL) operates under a weak supervision framework, where each sample is annotated by excluding several incorrect labels, known as complementary labels (CLs). Despite reducing ...

Rethinking deep clustering paradigms: Self-supervision is all you need.

Neural networks : the official journal of the International Neural Network Society
The recent advances in deep clustering have been made possible by significant progress in self-supervised and pseudo-supervised learning. However, the trade-off between self-supervision and pseudo-supervision can give rise to three primary issues. Th...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

Artificial intelligence in medicine
The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing disease ...

SimLVSeg: Simplifying Left Ventricular Segmentation in 2-D+Time Echocardiograms With Self- and Weakly Supervised Learning.

Ultrasound in medicine & biology
OBJECTIVE: Achieving reliable automatic left ventricle (LV) segmentation from echocardiograms is challenging due to the inherent sparsity of annotations in the dataset, as clinicians typically only annotate two specific frames for diagnostic purposes...

SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification.

International journal of molecular sciences
The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used...

DCST: Dual Cross-Supervision for Transformer-based Unsupervised Domain Adaptation.

Neural networks : the official journal of the International Neural Network Society
Unsupervised Domain Adaptation aims to leverage a source domain with ample labeled data to tackle tasks on an unlabeled target domain. However, this poses a significant challenge, particularly in scenarios exhibiting significant disparities between t...