AIMC Topic: Supervised Machine Learning

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Uncertainty guided semi-supervised few-shot segmentation with prototype level fusion.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Semantic Segmentation (FSS) aims to tackle the challenge of segmenting novel categories with limited annotated data. However, given the diversity among support-query pairs, transferring meta-knowledge to unseen categories poses a significant...

Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method.

Science China. Life sciences
Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Streptococcus thermophilus (S. thermophilus) are commonly used starters in milk fermentation. Fermentation experiments revealed that L. bulgaricus-S. thermophilus interactions (LbStI) su...

Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning.

Journal of the American Society for Mass Spectrometry
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragme...

GSSCL: A framework for Graph Self-Supervised Curriculum Learning based on clustering label smoothing.

Neural networks : the official journal of the International Neural Network Society
Graph self-supervised learning is an effective technique for learning common knowledge from unlabeled graph data through pretext tasks. To capture the interrelationships between nodes and their essential roles globally, existing methods use clusterin...

Drug-target prediction through self supervised learning with dual task ensemble approach.

Computational biology and chemistry
Drug-Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of exist...

Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...

Bootstrap each lead's latent: A novel method for self-supervised learning of multilead electrocardiograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools for cardiovascular diseases (CVDs). Recent studies show that deep learning models can be trained using labeled ECGs to achieve automatic detection of CVDs...

A Knowledge-Driven Self-Supervised Approach for Molecular Generation.

IEEE/ACM transactions on computational biology and bioinformatics
Due to the great successes of Graph Neural Networks (GNN) in numerous fields, growing research interests have been devoted to applying GNN to molecular learning tasks. The molecule structure can be naturally represented as graphs where atoms and bond...

Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning.

Computers in biology and medicine
Diagnosing dental caries poses a significant challenge in dentistry, necessitating precise and early detection for effective management. This study utilizes Self-Supervised Learning (SSL) tasks to improve the classification of dental caries in Cone B...

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...