about
sameAs
Open source software projects of the caBIG In Vivo Imaging Workspace Software special interest groupConsensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trialsImage sharing: evolving solutions in the age of interoperabilityThe basics of diffusion and perfusion imaging in brain tumorsThe RSNA Image Sharing NetworkImaging in clinical trialsSupervised segmentation of polycystic kidneys: a new application for stereology data.Multisite Image Data Collection and Management Using the RSNA Image Sharing Network.Performance and function of a desktop viewer at Mayo Clinic Scottsdale.A review of the automated detection of change in serial imaging studies of the brain.Evaluation of irreversible JPEG compression for a clinical ultrasound practice.Imaging of remyelination and neuronal health.Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing.Electronic imaging and clinical implementation: work group approach at Mayo Clinic, Rochester.Emerging trends in the volume and format of outside examinations submitted for secondary interpretationTeaching NeuroImages: demyelinating disease mimicking butterfly high-grade glioma.Imaging infrastructure for research. Part 2. Data management practices.Imaging classification of autosomal dominant polycystic kidney disease: a simple model for selecting patients for clinical trials.Addressing the coming radiology crisis-the Society for Computer Applications in Radiology transforming the radiological interpretation process (TRIP) initiative.The electronic imaging technology specialist: the role of a new radiology subspecialty for the 21st century.Computer-aided detection and diagnosis at the start of the third millenniumCharacteristics of dynamic magnetic resonance image enhancement in prolactinomas resistant to dopamine agonist therapy.Validation of neuroradiologic response assessment in gliomas: measurement by RECIST, two-dimensional, computer-assisted tumor area, and computer-assisted tumor volume methods.Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging.Diffusion-weighted imaging characteristics of biopsy-proven demyelinating brain lesionsMIRMAID: A Content Management System for Medical Image Analysis ResearchImpact of electronic imaging on clinician behavior in the urgent care settingElectronic imaging impact on image and report turnaround times.An algorithm for automatic segmentation and classification of magnetic resonance brain imagesEvaluation of the accuracy of a continuous speech recognition software system in radiology.Toolkits and Libraries for Deep Learning.Initial evaluation of a continuous speech recognition program for radiology.Whitepapers on imaging infrastructure for research part three: security and privacyDeep Learning for Brain MRI Segmentation: State of the Art and Future DirectionsAutomatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression.Standards for business analytics and departmental workflow.MRI texture features as biomarkers to predict MGMT methylation status in glioblastomasFLAIR histogram segmentation for measurement of leukoaraiosis volume.Optimal presentation modes for detecting brain tumor progression.Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning.
P50
Q24676773-998471CD-099C-4DB6-A7E6-C7075933D7C7Q26801351-46C1580D-FDCC-433B-BB1E-1478497F1A6FQ26865851-7E4B0D04-2305-41BB-BE09-DEDC30FEE822Q28606582-416FCFD0-0482-4DC2-B77C-4919912D0B83Q28651582-C599B77B-8A20-4D2A-BAEA-6AE90F87EDDEQ28759581-33DE6EE0-6204-4DBB-AA58-C629F2C912ABQ30780055-4EE6C67C-8481-45EE-A608-BB1717778603Q30810414-14B54763-3E9E-4BD7-8520-96F1BF126D61Q30877776-5FFF7D73-1845-4AF0-991E-AA96254614A5Q30975784-7D6C8817-4A72-4E65-8037-0F66B5BE0584Q31097512-B4EEC047-E8D1-4B40-82B5-3E3F62A07FC4Q31144027-6149D8D6-16DF-4B4C-AE3D-B505759B5B3BQ33457236-7BCC97BC-B6AB-4C23-962F-B88F3FD66BAAQ33643671-23D0EE68-E3A1-4646-B4EA-AB1C247554C7Q33647863-F52E9283-AE07-4FC9-AB1B-DC4736D546A8Q34072728-3F6B2480-726F-43EB-B37E-52815BCC01B5Q34308327-3CBA5F1C-B225-4AA2-AF53-7EC9E095A677Q34423262-EBCE7DBF-C1B0-4E00-8FB9-29481D7718FEQ34616925-CE0951AF-36E3-465B-95B8-AB0C1FD6B13BQ34726879-3078B80C-E8D9-4541-97D0-3FDA80A26DCBQ34896284-6D111896-086F-4188-9E77-897FB93B5572Q35106933-5969057B-053A-4CC9-BEA1-536F922FD8E9Q35800569-B619E660-2693-43F0-B240-769E937D3FD6Q35950898-D049DB31-9EA4-4F5D-BC0C-3694615587EFQ35985549-94BB6F12-07E3-4681-B398-A7B1AC8355ACQ36185827-006F1E30-2D71-41EF-BA4E-7FCA3D088F3AQ36262633-59C8198F-282B-45A4-AF52-D536A37537FBQ36262643-C4703DB5-254B-4BF4-8244-4CB279589E2AQ36262820-2E1B63AA-8CED-40F6-B38C-7DB948167307Q36263583-BA5721FE-BDFB-4A29-B569-BB77615F1217Q36313543-217C6EB3-98BF-429A-994C-47482B03EEF7Q36371904-78F8FEF8-CE4E-44F4-B099-577FC86E1C00Q36377331-F18B2C29-F914-4999-A15F-4C1F4228394AQ36391630-A991C1BC-1FDC-4397-A648-414910CDD7C1Q36497881-8B02C903-330E-4E39-B37A-F86E1FE89C18Q36557350-0F29B71A-9E79-4FB8-BF33-ED6642B5B69FQ36901118-96C735ED-7874-427F-AFBD-777BC9A77E94Q37371098-87657FBF-0A0D-4E1B-887E-8CAB10D007DBQ37540029-CA2C8854-D2C6-4FF9-A817-DB110EE81214Q37561820-66D59640-4356-4CDC-8AA2-D63B63AC2EB5
P50
description
Forscher
@de
chercheur
@fr
investigador
@es
researcher
@en
ricercatore
@it
wetenschapper
@nl
研究者
@zh
name
Bradley J Erickson
@ast
Bradley J Erickson
@en
Bradley J Erickson
@es
Bradley J Erickson
@nl
type
label
Bradley J Erickson
@ast
Bradley J Erickson
@en
Bradley J Erickson
@es
Bradley J Erickson
@nl
altLabel
B J Erickson
@en
prefLabel
Bradley J Erickson
@ast
Bradley J Erickson
@en
Bradley J Erickson
@es
Bradley J Erickson
@nl
P108
P31
P496
0000-0001-7926-6095