相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning
Sauson Soldozy et al.
WORLD NEUROSURGERY (2021)
An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning
Joeky T. Senders et al.
NEUROSURGERY (2020)
The impact of machine learning on patient care: A systematic review
David Ben-Israel et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)
Attitudes of Patients and Their Relatives Toward Artificial Intelligence in Neurosurgery
Paolo Palmisciano et al.
WORLD NEUROSURGERY (2020)
Machine learning in neurosurgery: a global survey
Victor E. Staartjes et al.
ACTA NEUROCHIRURGICA (2020)
Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI
Renato Cuocolo et al.
NEURORADIOLOGY (2020)
Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI
Amalya Zeynalova et al.
NEURORADIOLOGY (2019)
A systematic review on machine learning in sellar region diseases: quality and reporting items
Nidan Qiao
ENDOCRINE CONNECTIONS (2019)
Pituitary Adenomas and Invasiveness from Anatomo-Surgical, Radiological, and Histological Perspectives: A Systematic Literature Review
Simona Serioli et al.
CANCERS (2019)
Evaluation of variable selection methods for random forests and omics data sets
Frauke Degenhardt et al.
BRIEFINGS IN BIOINFORMATICS (2019)
Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity
Raji Ghawi et al.
OPEN COMPUTER SCIENCE (2019)
Identification and repair of intraoperative cerebrospinal fluid leaks in endonasal transsphenoidal pituitary surgery: surgical experience in a series of 1002 patients
Ben A. Strickland et al.
JOURNAL OF NEUROSURGERY (2018)
Preoperative risk factors for postoperative complications in endoscopic pituitary surgery: a systematic review
Daniel J. Lobatto et al.
PITUITARY (2018)
Trusting telemedicine: A discussion on risks, safety, legal implications and liability of involved stakeholders
E. Parimbelli et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2018)
Primary versus revision transsphenoidal resection for nonfunctioning pituitary macroadenomas: matched cohort study
Colin J. Przybylowski et al.
JOURNAL OF NEUROSURGERY (2017)
Comparison between Random Forests, Artificial Neural Networks and Gradient Boosted Machines Methods of On-Line Vis-NIR Spectroscopy Measurements of Soil Total Nitrogen and Total Carbon
Said Nawar et al.
SENSORS (2017)
Cerebrospinal fluid rhinorrhoea following transsphenoidal surgery for pituitary adenoma: experience in a Chinese centre
C. Zhang et al.
ACTA OTORHINOLARYNGOLOGICA ITALICA (2017)
Factors impacting cerebrospinal fluid leak rates in endoscopic sellar surgery
Tom T. Karnezis et al.
INTERNATIONAL FORUM OF ALLERGY & RHINOLOGY (2016)
Pituitary MRI characteristics in 297 acromegaly patients based on T2-weighted sequences
Iulia Potorac et al.
ENDOCRINE-RELATED CANCER (2015)
Invasion of the cavernous sinus space in pituitary adenomas: endoscopic verification and its correlation with an MRI-based classification
Alexander S. G. Micko et al.
JOURNAL OF NEUROSURGERY (2015)
Identifying representative trees from ensembles
Mousumi Banerjee et al.
STATISTICS IN MEDICINE (2012)
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome
Remi Cuingnet et al.
MEDICAL IMAGE ANALYSIS (2011)
Boruta - A System for Feature Selection
Miron B. Kursa et al.
FUNDAMENTA INFORMATICAE (2010)
Endoscopic pituitary surgery: a systematic review and meta-analysis Clinical article
Abtin Tabaee et al.
JOURNAL OF NEUROSURGERY (2009)
Risk factors of cerebrospinal fluid rhinorrhea following transsphenoidal surgery
H Nishioka et al.
ACTA NEUROCHIRURGICA (2005)
Incidence, etiology, and management of cerebrospinal fluid leaks following trans-sphenoidal surgery
SG Shiley et al.
LARYNGOSCOPE (2003)
Benchmarking attribute selection techniques for discrete class data mining
MA Hall et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2003)
A review of evidence of health benefit from artificial neural networks in medical intervention
PJG Lisboa
NEURAL NETWORKS (2002)
Artificial intelligence applications in the intensive care unit
CW Hanson et al.
CRITICAL CARE MEDICINE (2001)