Items tagged “artificial intelligence”
16 results found
Ensembling (sometimes ensemble learning) is a class of meta-algorithmic techniques where multiple models are trained and their results are aggregated to improve classification performance. It is effective in a wide variety of problems. Two commonly used methods are: boosting: a method of wei...
Radiology informatics, also referred to as imaging informatics, is a subspecialty of radiology concerned with applying information science to radiology. Imaging informatics is part of the larger field of clinical informatics which is in turn part of biomedical informatics. Radiology informatics ...
Bagging is a term often used in the fields of machine learning, data science and computational statistics that refers to bootstrap aggregation. Bootstrapped aggregation of data can be employed in many different AI (artificial intelligence) algorithms, and is often a necessary step to making rand...
Random forest (machine learning)
Random Forest also known as random decision forests are a specific type of ensembling algorithm that utilizes a combination of decision trees based on subsets of a dataset. A random forest algorithm does not make a decision tree of smaller decision trees, but rather utilizes decision trees in pa...
Selection bias is a type of bias created when the data sampled is not representative of the data of the population or group that a study or model aims to make a prediction about. Selection bias is the result of systematic errors in data selection and collection. Practically-speaking selection bi...
Confusion matrices, a key tool to evaluate machine learning algorithm performance in classification, are a statistical tool. Contingency tables, a type of confusion matrix, are used in the evaluation of many diagnostic exams for sensitivity, specificity, positive and negative predictive values....
Artificial Intelligence (AI) TI-RADS
AI TI-RADS (Artificial Intelligence Thyroid Imaging Reporting and Data System) is a data-driven analysis and revision of the 2017 ACR TI-RADS 1. Published in May 2019 2, this had the intention of simplifying categorization and improving specificity while maintaining high sensitivity. This system...
Optimization algorithms are widely utilized mathematical functions that solve problems via the maximization or minimization of a function. These algorithms are used for a variety of purposes from patient scheduling to radiology. Machine learning Optimization algorithms are used in machine lea...
Information leakage is one of the common and important errors in data handling during all machine learning applications, including those in radiology. Briefly, it means the incomplete separation of the training, validation, and testing datasets, which can significantly change the apparent perfor...
Generalisability in machine learning models represents how well the models can be adapted to new example datasets. Evaluating generalisability of machine learning applications is crucial as this has profound implications for their clinical adaptability. Briefly, two main techniques are used fo...
Heatmap - intracranial hemorrhage
Diagnosis not applicable
Published 17 Apr 2021
Deep learning is a subset of machine learning based on multi-layered (a.k.a. “deep“) artificial neural networks. Their highly flexible architectures can learn directly from data (such as images, video or text) without the need of hand-coded rules and can increase their predictive accuracy when p...
The ImageNet is an extensive image database which has been instrumental in advancing computer vision and deep learning research. It contains more than 14 million, hand-annotated images classified into more than 20,000 categories. In at least one million of the images, bounding boxes are also pro...
Learning curve (machine learning)
A learning curve is a plot of the learning performance of a machine learning model (usually measured as loss or accuracy) over time (usually in a number of epochs). Learning curves are a widely used diagnostic tool in machine learning to get an overview of the learning and generalization behavi...
Autoencoders are an unsupervised learning technique in which artificial neural networks are used to learn to produce a compressed representation of the input data. Essentially, autoencoding is a data compression algorithm where the compression and decompression functions are learned automatical...
Hebbian learning describes a type of activity-dependent modification of the strength of synaptic transmission at pre-existing synapses which plays a central role in the capacity of the brain to convert transient experiences into memory. According to Hebb et al 1, two cells or systems of cells th...