Cole J. Cook
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View article: Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation
Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation Open
Radiological protocol selection is a critical but time-consuming step in clinical workflow, requiring radiologists to match patient indications with the appropriate MRI or CT protocol. Manual selection can be prone to delays or potential e…
View article: Evaluating prompt and data perturbation sensitivity in large language models for radiology reports classification
Evaluating prompt and data perturbation sensitivity in large language models for radiology reports classification Open
Objectives Large language models (LLMs) offer potential in natural language processing tasks in healthcare. Due to the need for high accuracy, understanding their limitations is essential. The purpose of this study was to evaluate the perf…
View article: A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images Open
View article: Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans
Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans Open
View article: Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance–Derived Measurement of Total Kidney Volume
Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance–Derived Measurement of Total Kidney Volume Open
Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.
View article: Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning
Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning Open
The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions. This may impact the majority of radiomic studies published recently and contribute to…
View article: Genetic influence on resting state networks in young male and female adults
Genetic influence on resting state networks in young male and female adults Open
Determining genetic versus environmental influences on the human brain is of crucial importance to understand the healthy brain as well as in a variety of disease and disorder states. Here we propose a unique, minimal assumption, approach …
View article: Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity
Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity Open
Brain gender differences have been known for a long time and are the possible reason for many psychological, psychiatric and behavioral differences between males and females. Predicting genders from brain functional connectivity (FC) can b…
View article: Brain aging in temporal lobe epilepsy: Chronological, structural, and functional
Brain aging in temporal lobe epilepsy: Chronological, structural, and functional Open
View article: IC‐P‐055: EVALUATING RESTING STATE CONNECTIVITY HERITABILITY AT THE WHOLE BRAIN LEVEL
IC‐P‐055: EVALUATING RESTING STATE CONNECTIVITY HERITABILITY AT THE WHOLE BRAIN LEVEL Open
Resting state networks (RSNs) have been associated with mental illness and disease and may serve as endophenotypes such as in Alzheimer's Disease. Criteria to define an endophenotype necessitates heritability. While several studies have in…
View article: P2‐386: EFFECTIVE CONNECTIVITY WITHIN THE LEFT AND RIGHT EXECUTIVE CONTROL NETWORKS IN MCI AND AD
P2‐386: EFFECTIVE CONNECTIVITY WITHIN THE LEFT AND RIGHT EXECUTIVE CONTROL NETWORKS IN MCI AND AD Open
The default mode network, implicated in various types of memory, is often investigated in Alzheimer's disease (AD). AD patients often exhibit both memory impairments and executive dysfunction. The executive control network (ECN), thought t…
View article: P1‐125: EVALUATING RESTING STATE CONNECTIVITY HERITABILITY AT THE WHOLE BRAIN LEVEL
P1‐125: EVALUATING RESTING STATE CONNECTIVITY HERITABILITY AT THE WHOLE BRAIN LEVEL Open
Resting state networks (RSNs) have been associated with mental illness and disease and may serve as endophenotypes such as in Alzheimer's Disease. Criteria to define an endophenotype necessitates heritability. While several studies have in…
View article: IC‐P‐024: EFFECTIVE CONNECTIVITY WITHIN THE LEFT AND RIGHT EXECUTIVE CONTROL NETWORKS IN MCI AND AD
IC‐P‐024: EFFECTIVE CONNECTIVITY WITHIN THE LEFT AND RIGHT EXECUTIVE CONTROL NETWORKS IN MCI AND AD Open
The default mode network, implicated in various types of memory, is often investigated in Alzheimer's disease (AD). AD patients often exhibit both memory impairments and executive dysfunction. The executive control network (ECN), thought t…
View article: Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project
Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project Open
The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using these data, the effective connectivity of the default mode network (DMN) in patients with lef…