AI Medical Compendium Topic:
Supervised Machine Learning

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A data-centric weak supervised learning for highway traffic incident detection.

Accident; analysis and prevention
Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leverag...

RPDNet: Automatic Fabric Defect Detection Based on a Convolutional Neural Network and Repeated Pattern Analysis.

Sensors (Basel, Switzerland)
On a global scale, the process of automatic defect detection represents a critical stage of quality control in textile industries. In this paper, a semantic segmentation network using a repeated pattern analysis algorithm is proposed for pixel-level ...

Critical analysis on the reproducibility of visual quality assessment using deep features.

PloS one
Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets. This paper illustrates that complex data leakage cases have occurred in the no-reference image and video quality assessment...

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.

Sensors (Basel, Switzerland)
Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual an...

Distributed contrastive learning for medical image segmentation.

Medical image analysis
Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) ca...

Semi-supervised learning framework for oil and gas pipeline failure detection.

Scientific reports
Quantifying failure events of oil and gas pipelines in real- or near-real-time facilitates a faster and more appropriate response plan. Developing a data-driven pipeline failure assessment model, however, faces a major challenge; failure history, in ...

Self-supervised learning in medicine and healthcare.

Nature biomedical engineering
The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale unannotated data provides opportunities for the development of better machine-learning ...

An Effective Semi-Supervised Approach for Liver CT Image Segmentation.

IEEE journal of biomedical and health informatics
Despite the substantial progress made by deep networks in the field of medical image segmentation, they generally require sufficient pixel-level annotated data for training. The scale of training data remains to be the main bottleneck to obtain a bet...

MTL-ABSNet: Atlas-Based Semi-Supervised Organ Segmentation Network With Multi-Task Learning for Medical Images.

IEEE journal of biomedical and health informatics
Organ segmentation is one of the most important step for various medical image analysis tasks. Recently, semi-supervised learning (SSL) has attracted much attentions by reducing labeling cost. However, most of the existing SSLs neglected the prior sh...

In-Field Automatic Identification of Pomegranates Using a Farmer Robot.

Sensors (Basel, Switzerland)
Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper pr...