AI Medical Compendium Topic:
Supervised Machine Learning

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Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.

Frontiers in public health
South Africa (SA) has the highest incidence of colorectal cancer (CRC) in Sub-Saharan Africa (SSA). However, there is limited research on CRC recurrence and survival in SA. CRC recurrence and overall survival are highly variable across studies. Accu...

Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean.

Medical image analysis
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets. Semi-supervised learning (SSL) approaches leverage small labeled data...

Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.

Journal of computer-aided molecular design
In the field of drug-target interactions prediction, the majority of approaches formulated the problem as a simple binary classification task. These methods used binary drug-target interaction datasets to train their models. The prediction of drug-ta...

Quantifying finer-scale behaviours using self-organising maps (SOMs) to link accelerometery signatures with behavioural patterns in free-roaming terrestrial animals.

Scientific reports
Collecting quantitative information on animal behaviours is difficult, especially from cryptic species or species that alter natural behaviours under observation. Using harness-mounted tri-axial accelerometers free-roaming domestic cats (Felis Catus)...

Self-paced and self-consistent co-training for semi-supervised image segmentation.

Medical image analysis
Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-trainin...

Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound.

Medical image analysis
Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method i...

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.

Nature protocols
Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly ...

Predictive analytics for step-up therapy: Supervised or semi-supervised learning?

Journal of biomedical informatics
BACKGROUND: Step-up therapy is a patient management approach that aims to balance the efficacy, costs and risks posed by different lines of medications. While the initiation of first line medications is a straightforward decision, stepping-up a patie...

Improving the Performance of Machine Learning-Based Network Intrusion Detection Systems on the UNSW-NB15 Dataset.

Computational intelligence and neuroscience
Networks are exposed to an increasing number of cyberattacks due to their vulnerabilities. So, cybersecurity strives to make networks as safe as possible, by introducing defense systems to detect any suspicious activities. However, firewalls and clas...

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization.

Sensors (Basel, Switzerland)
Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localizatio...