AI Medical Compendium Topic:
Supervised Machine Learning

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Uncertainty-Guided Voxel-Level Supervised Contrastive Learning for Semi-Supervised Medical Image Segmentation.

International journal of neural systems
Semi-supervised learning reduces overfitting and facilitates medical image segmentation by regularizing the learning of limited well-annotated data with the knowledge provided by a large amount of unlabeled data. However, there are many misuses and u...

Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach.

Sensors (Basel, Switzerland)
Hyperspectral remote sensing has tremendous potential for monitoring land cover and water bodies from the rich spatial and spectral information contained in the images. It is a time and resource consuming task to obtain groundtruth data for these ima...

The Challenge of Data Annotation in Deep Learning-A Case Study on Whole Plant Corn Silage.

Sensors (Basel, Switzerland)
Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed proces...

Distributed Information-Theoretic Semisupervised Learning for Multilabel Classification.

IEEE transactions on cybernetics
Multilabel classification (MLC) has received much attention recently. The existing MLC algorithms usually learn multiple classifiers simultaneously by exploiting the correlations among different labels. However, it is difficult and/or expensive to co...

Intra- and Inter-Slice Contrastive Learning for Point Supervised OCT Fluid Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks achieve great success in OCT fluid segmentation. However, requiring pixel-w...

A machine learning-based on-demand sweat glucose reporting platform.

Scientific reports
Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and altering carbohydrate metabolism. It is a leading cause of morbidity, resulting in a reduced quality of life even in developed societies, primarily affected...

Incremental learning algorithm for large-scale semi-supervised ordinal regression.

Neural networks : the official journal of the International Neural Network Society
As a special case of multi-classification, ordinal regression (also known as ordinal classification) is a popular method to tackle the multi-class problems with samples marked by a set of ranks. Semi-supervised ordinal regression (SSOR) is especially...

DynaMorph: self-supervised learning of morphodynamic states of live cells.

Molecular biology of the cell
A cell's shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human...

Optimal Architecture of Floating-Point Arithmetic for Neural Network Training Processors.

Sensors (Basel, Switzerland)
The convergence of artificial intelligence (AI) is one of the critical technologies in the recent fourth industrial revolution. The AIoT (Artificial Intelligence Internet of Things) is expected to be a solution that aids rapid and secure data process...

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors.

Sensors (Basel, Switzerland)
This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, fo...