Results 61 to 70 of about 1,347,746 (312)
Deep learning algorithms (BERT Baseline F1-Score, Kappa Score, and Accuracy).
, 2023 Deep learning algorithms (BERT Baseline F1-Score, Kappa Score, and Accuracy).
Abdul Ghafoor (849371) +5 more
core +1 more source
FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source
Deep learning algorithms (VADER Baseline F1-Score, Kappa Score, and Accuracy).
, 2023 Deep learning algorithms (VADER Baseline F1-Score, Kappa Score, and Accuracy).
Abdul Ghafoor (849371) +5 more
core +1 more source
Developmental and Epileptic Encephalopathy due to Biallelic Pathogenic Variants in PIGM
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective PIGM encodes a critical enzyme in the glycosylphosphatidylinositol (GPI)‐anchor biosynthesis pathway. While promoter‐region mutations in PIGM have been associated with a relatively mild phenotype characterized by portal vein thrombosis and absence seizures, recent evidence suggests that coding‐region mutations result in a more severe
Júlia Sala‐Coromina +11 more
wiley +1 more source
Comparison of Different Machine Learning Algorithms to Classify Epilepsy Seizure from EEG Signals
Engineering ProceedingsRecurrent seizures are a symptom of a central nervous system disease called epilepsy. The duration of these seizures lasts less than a few seconds or sometimes minutes. There are very few ways to record seizures, and one of them is EEG.
Pankaj Kunekar +5 more
doaj +1 more source
Estimating the $$F_1$$ Score for Learning from Positive and Unlabeled Examples [PDF]
, 2020 Semi-supervised learning can be applied to datasets that contain both labeled and unlabeled instances and can result in more accurate predictions compared to fully supervised or unsupervised learning in case limited labeled data is available. A subclass of problems, called Positive-Unlabeled (PU) learning, focuses on cases in which the labeled ...
S.A. Tabatabaei (Seyed Amin) +2 more
openaire +2 more sources
Deep algorithms (Weakly supervised Baseline F1-Score, Kappa Score, and Accuracy).
, 2023 Deep algorithms (Weakly supervised Baseline F1-Score, Kappa Score, and Accuracy).
Abdul Ghafoor (849371) +5 more
core +1 more source
Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig +9 more
wiley +1 more source
Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
Applied SciencesDeveloping interfaces for seizure diagnosis, often challenging to detect visually, is rising. However, their effectiveness is constrained by the need for diverse and extensive databases.
Laura López-Viñas +2 more
doaj +1 more source
Monthly Notices of the Royal Astronomical Society: Letters, 2022 ABSTRACT Machine-learning based classifiers have become indispensable in the field of astrophysics, allowing separation of astronomical sources into various classes, with computational efficiency suitable for application to the enormous data volumes that wide-area surveys now typically produce.
Humphrey, A. +7 more
openaire +2 more sources