Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know

MW Wagner, K Namdar, A Biswas, S Monah, F Khalvati… - Neuroradiology, 2021‏ - Springer
Purpose Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology.
Methods When designing AI-based research in neuroradiology and appreciating the …

Systematic review of artificial intelligence for abnormality detection in high-volume neuroimaging and subgroup meta-analysis for intracranial hemorrhage detection

S Agarwal, D Wood, M Grzeda, C Suresh, M Din… - Clinical …, 2023‏ - Springer
Purpose Most studies evaluating artificial intelligence (AI) models that detect abnormalities
in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently …

Computational approaches for acute traumatic brain injury image recognition

E Lin, EL Yuh - Frontiers in neurology, 2022‏ - frontiersin.org
In recent years, there have been major advances in deep learning algorithms for image
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …

Faster and better: how anomaly detection can accelerate and improve reporting of head computed tomography

T Finck, J Moosbauer, M Probst, S Schlaeger… - Diagnostics, 2022‏ - mdpi.com
Background: Most artificial intelligence (AI) systems are restricted to solving a pre-defined
task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves …

Denoising Diffusion Models for Anomaly Localization in Medical Images

CI Bercea, PC Cattin, JA Schnabel, J Wolleb - arxiv preprint arxiv …, 2024‏ - arxiv.org
This chapter explores anomaly localization in medical images using denoising diffusion
models. After providing a brief methodological background of these models, including their …

Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis

S Agarwal, DA Wood, M Grzeda, C Suresh… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities
in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently …