Computer-aided diagnosis: how to move from the laboratory to the clinic.
about
Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT ScansImproving precision medicine using individual patient data from trials.Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.Diagnostic accuracy of computer-aided detection of pulmonary tuberculosis in chest radiographs: a validation study from sub-Saharan AfricaClinical evaluation of semi-automatic landmark-based lesion tracking software for CT-scans.Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.Computer-aided classification of lung nodules on computed tomography images via deep learning techniqueThe future of medical diagnostics: large digitized databases.Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.Computer-aided diagnosis program for classifying the risk of hepatocellular carcinoma on MR images following liver imaging reporting and data system (LI-RADS).Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET.Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomographyProgress in Fully Automated Abdominal CT Interpretation.Differentiation of benign pigmented skin lesions with the aid of computer image analysis: a novel approach.Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms.Computer-aided detection system for lung cancer in computed tomography scans: review and future prospectsRelevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.Advances and perspectives in lung cancer imaging using multidetector row computed tomography.Bone age assessment: automated techniques coming of age?Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography.Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.Computer-Aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.Quantitative functional MRI biomarkers improved early detection of colorectal liver metastases.Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learningTechnical Note: Image filtering to make computer-aided detection robust to image reconstruction kernel choice in lung cancer CT screening.Automated Segmentation of Light-Sheet Fluorescent Imaging to Characterize Experimental Doxorubicin-Induced Cardiac Injury and Repair.Three-dimensional morphological and signal intensity features for detection of intervertebral disc degeneration from magnetic resonance images.Radiomics in paediatric neuro-oncology: A multicentre study on MRI texture analysis.Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks.Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm.A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetectDevelopment and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT.Automatic detection of large pulmonary solid nodules in thoracic CT images.Toward clinically usable CAD for lung cancer screening with computed tomography.[Future of mammography-based imaging].Automated Segmentation Methods of Drusen to Diagnose Age-Related Macular Degeneration Screening in Retinal Images.
P2860
Q30385578-590D9BF9-DA7C-4443-BE9A-7EACBD00C287Q31125430-7C656D9D-7455-4FB5-BBFB-FFD963F4FFF1Q34030115-F3570D61-BD2E-44F5-A7CF-B9C6AFB33038Q34142288-75CE614D-923E-4775-83C4-DA4F09BCDBF4Q34419041-A412328D-B034-4C53-8CBB-CA91615C137BQ35085668-42B443C6-4F3E-45BC-BBD3-45AF73E5F07FQ35934886-A937B52B-A3CE-48DB-8013-30F4627E6971Q36247947-9C1F069F-2843-42CD-97EB-A99AD88A7584Q36377361-A78717AA-60F3-4F8B-80E9-313BF9D9237AQ36384510-A780FA18-8E59-4195-9A7B-3A1423542507Q36736616-968AD657-765A-4D67-8A53-6127DCE30C72Q36907044-9980F7B6-A07C-464A-AB53-FA249EFDCB58Q37033770-300E74E9-D2DD-4A9A-B0B6-AFE31F98690CQ37129372-5C7292F6-FB2F-4F9A-B8EE-6CA1EAA41671Q37399486-CE0AC797-3441-4C74-951B-3EE111380CE9Q37716445-78B21C13-06DC-4312-AA91-832BB3E251ACQ37737882-09A085C6-71E4-4798-B64B-7C561349BD6AQ38062019-A93B6E68-2AC2-4C4A-8BBE-FF14503E1CCCQ38157840-D0FC6180-C47D-43EE-875F-B8705912E019Q38610825-5F7446F9-A6F4-4007-8ED4-3809AD50AEE9Q38616303-4B71DE0B-5BD3-45F4-AE4E-41A40063AF02Q38733783-6C18A729-128C-474D-B639-42F14C593B1DQ38812950-0811BEDC-31F5-4080-91E2-EEF0E78160F1Q39017059-83A4F3C6-FA81-4F0A-AD2B-BA431F20B6C7Q39100110-63AEB878-5203-4974-A893-D8BD81328373Q39140311-ED507EF2-52CC-45A2-A55C-CE5C19786C5AQ39637879-29BCF5E1-9A44-4239-BA96-8946426ADE4CQ41447176-FD2E7BFD-3447-40FD-8CB8-F2C6B6C6E84BQ42064585-742B15FD-2761-475B-80EE-346FF6C65776Q45944454-29AA4CDB-9131-4145-9646-835826B13C30Q45945051-1E5E94C1-0A8F-4BBC-9289-228DA72DAD45Q45947750-9C7C68FF-D9D2-4CE6-8E8A-78BE25DFAC4DQ47339047-EB064883-A45B-4801-905C-A6FA1FD6FE79Q47418330-D61EBA35-DEE0-4B97-862E-4A8556206220Q47935784-6A78825C-C853-4B34-8DA5-C0E722A42394Q49588242-0645CF56-2133-40CF-B9FD-88A82AD86C54Q50800712-11C84984-AF76-4755-BA3B-88F53CBD558EQ53059147-CA76E998-5BBE-4814-95C5-F86C26EDB6B5Q53077783-3287E2BA-7C27-4F9D-B496-B1A32E11E439Q55082927-AD191BD7-E652-4BD3-80B3-0A1F9CB186DF
P2860
Computer-aided diagnosis: how to move from the laboratory to the clinic.
description
article científic
@ca
article scientifique
@fr
articol științific
@ro
articolo scientifico
@it
artigo científico
@gl
artigo científico
@pt
artigo científico
@pt-br
artikel ilmiah
@id
artikull shkencor
@sq
artículo científico
@es
name
Computer-aided diagnosis: how to move from the laboratory to the clinic.
@en
Computer-aided diagnosis: how to move from the laboratory to the clinic.
@nl
type
label
Computer-aided diagnosis: how to move from the laboratory to the clinic.
@en
Computer-aided diagnosis: how to move from the laboratory to the clinic.
@nl
prefLabel
Computer-aided diagnosis: how to move from the laboratory to the clinic.
@en
Computer-aided diagnosis: how to move from the laboratory to the clinic.
@nl
P356
P1433
P1476
Computer-aided diagnosis: how to move from the laboratory to the clinic.
@en
P2093
Cornelia M Schaefer-Prokop
Mathias Prokop
P304
P356
10.1148/RADIOL.11091710
P407
P577
2011-12-01T00:00:00Z