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Medical Physics, ISSN 0094-2405, 12/2016, Volume 43, Issue 12, pp. 6654 - 6666
Journal Article
Medical Physics, ISSN 0094-2405, 06/2016, Volume 43, Issue 6, pp. 2821 - 2827
Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity;... 
false‐positive reduction | Medical image quality | Scintigraphy | nodule | Digital computing or data processing equipment or methods, specially adapted for specific applications | Computed tomography | feature extraction | Computer aided diagnosis | Computerised tomographs | medical image processing | Positron emission tomography (PET) | support vector machines | convolutional neural network | Artificial neural networks | Medical image segmentation | Radiologists | Biological material, e.g. blood, urine; Haemocytometers | Inference methods or devices | lung | computer‐aided detection | computerised tomography | Measuring half‐life of a radioactive substance | Lungs | Image data processing or generation, in general | PET/CT | cancer | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | positron emission tomography | false-positive reduction | computer-aided detection | TOMOGRAPHY | GRAPH | CT IMAGES | CANCER | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | LUNG-TUMOR SEGMENTATION | Radiographic Image Interpretation, Computer-Assisted - methods | Humans | Sensitivity and Specificity | Female | Lung Neoplasms - diagnostic imaging | Male | ROC Curve | Lung - diagnostic imaging | Support Vector Machine | Whole Body Imaging - methods | Neural Networks (Computer) | Positron Emission Tomography Computed Tomography - methods | NEURAL NETWORKS | IMAGES | POSITRON COMPUTED TOMOGRAPHY | RADIATION PROTECTION AND DOSIMETRY | SENSITIVITY | 60 APPLIED LIFE SCIENCES | CAT SCANNING
Journal Article
Medical Physics, ISSN 0094-2405, 06/2016, Volume 43, Issue 6, pp. 2835 - 2844
Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients,... 
biomedical MRI | glioblastoma multiforme | image classification | MRI | Entropy | random forest | Tissues | Medical image contrast | Digital computing or data processing equipment or methods, specially adapted for specific applications | genetics | Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging | Clinical applications | imaging biomarkers | tumours | medical image processing | Anatomic imaging | Image scanners | support vector machines | MGMT | Analysis of texture | Medical image segmentation | Biological material, e.g. blood, urine; Haemocytometers | brain | image texture | Inference methods or devices | Image analysis | Magnetic resonance imaging | Image data processing or generation, in general | decision trees | Machine learning | Germanium | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | Cancer | PROMOTER METHYLATION | APPARENT DIFFUSION-COEFFICIENT | CURVES | TEMOZOLOMIDE | MAGNETIC-RESONANCE | RADIOTHERAPY | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | PLUS | Promoter Regions, Genetic | Brain - diagnostic imaging | Brain Neoplasms - diagnostic imaging | Humans | DNA Repair Enzymes - genetics | Brain Neoplasms - genetics | Magnetic Resonance Imaging - methods | Support Vector Machine | Brain Neoplasms - surgery | Glioblastoma - surgery | DNA Methylation | DNA Modification Methylases - genetics | Glioblastoma - diagnostic imaging | Glioblastoma - genetics | Tumor Suppressor Proteins - genetics | Brain - surgery | ROC Curve | Biomarkers, Tumor - genetics | Retrospective Studies | GLIOMAS | METHYLATION | TEXTURE | NMR IMAGING | BIOMEDICAL RADIOGRAPHY | IMAGES | BIOLOGICAL MARKERS | RADIATION PROTECTION AND DOSIMETRY | METHYL TRANSFERASES | 60 APPLIED LIFE SCIENCES | QUANTITATIVE IMAGING AND IMAGE PROCESSING
Journal Article
Medical Physics, ISSN 0094-2405, 05/2016, Volume 43, Issue 5, pp. 2040 - 2052
Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models... 
sensitivity analysis | Biomedical modeling | regression analysis | random forest | neural network | Digital computing or data processing equipment or methods, specially adapted for specific applications | Learning | Mars | Dosimetry | learning (artificial intelligence) | Decision trees | toxicology | Data analysis | support vector machines | Probability theory, stochastic processes, and statistics | Inference methods or devices | elastic‐net | support‐vector machine | Statistical properties | Statistical model calculations | normal tissue complications | radiation therapy | cancer | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | Therapeutic applications, including brachytherapy | medical computing | Computer modeling | elastic-net | support-vector machine | ANDROGEN SUPPRESSION | TROG 03.04 RADAR | RANDOM FORESTS | INTENSITY-MODULATED RADIOTHERAPY | PHASE-3 FACTORIAL TRIAL | RADIATION-INDUCED PNEUMONITIS | VARIABLE SELECTION | ZOLEDRONIC ACID | DOSE-SURFACE MAPS | QUALITY-OF-LIFE | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Severity of Illness Index | Multivariate Analysis | Urination Disorders - diagnosis | Prostatic Neoplasms - radiotherapy | Prognosis | Follow-Up Studies | Area Under Curve | Comorbidity | Humans | Middle Aged | Prostate - radiation effects | Logistic Models | Male | Radiotherapy - adverse effects | Machine Learning | Models, Biological | Urination Disorders - etiology | Radiotherapy - methods | Aged, 80 and over | ROC Curve | Aged | Prostatic Neoplasms - complications | Neural Networks (Computer) | NEURAL NETWORKS | PROSTATE | MULTIVARIATE ANALYSIS | LEARNING | MARS PLANET | PERFORMANCE | RADIATION PROTECTION AND DOSIMETRY | RADIATION DOSES | SYMPTOMS | RADIOTHERAPY | COMPARATIVE EVALUATIONS | 60 APPLIED LIFE SCIENCES
Journal Article
Medical Physics, ISSN 0094-2405, 01/2016, Volume 43, Issue 1, pp. 554 - 567
Purpose: To develop a semiautomated computer‐aided diagnosis (cad) system for thyroid cancer using two‐dimensional ultrasound images that can be used to yield... 
Medical image noise | image classification | biomedical ultrasonics | thyroid cancer | Multivariate analysis | Digital computing or data processing equipment or methods, specially adapted for specific applications | feature extraction | image segmentation | Ultrasonography | computer‐aided diagnosis | Medical diagnosis with acoustics | SVM classifier | feature selection | medical image processing | support vector machines | Diagnosis using ultrasonic, sonic or infrasonic waves | Medical image segmentation | Radiologists | Biological material, e.g. blood, urine; Haemocytometers | Inference methods or devices | textural features | Image data processing or generation, in general | Anisotropy | Optical inspection | Flow visualization | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | Cancer | computer-aided diagnosis | LESION CLASSIFICATION | SYSTEM | ELASTOGRAPHY | COMBINATION | FEATURES | TEXTURE | MATRICES | SEGMENTATION | DIFFERENTIATION | ULTRASONOGRAPHY | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Diagnosis, Differential | Diagnosis, Computer-Assisted | Humans | Signal-To-Noise Ratio | Thyroid Nodule - diagnostic imaging | Radiology | ROC Curve | Image Processing, Computer-Assisted - methods | DIAGNOSIS | NEOPLASMS | BIOMEDICAL RADIOGRAPHY | COMPUTERS | RADIATION PROTECTION AND DOSIMETRY | BIOPSY | THYROID | TWO-DIMENSIONAL SYSTEMS | VALIDATION | ACCURACY | MULTIVARIATE ANALYSIS | IMAGES | RADIOLOGY AND NUCLEAR MEDICINE | AXIAL RATIO
Journal Article
Medical Physics, ISSN 0094-2405, 12/2016, Volume 43, Issue 12, pp. 6439 - 6454
Purpose: At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based... 
quantitative ultrasound | cellular biophysics | Backscattering | Ultrasonic effects | biomedical ultrasonics | Digital computing or data processing equipment or methods, specially adapted for specific applications | cancer therapy | Ultrasound therapy | feature extraction | Ultrasonography | Fluid bubbles | Dosimetry | learning (artificial intelligence) | tumours | computer aided prognosis | medical image processing | Radiofrequency spectra | Therapeutic applications | Diagnosis using ultrasonic, sonic or infrasonic waves | Radiation treatment | Analysis of texture | Cell processes | ultrasonic therapy | image texture | Inference methods or devices | microbubbles | Image data processing or generation, in general | radiation therapy | kernel methods | cancer | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | SOLID TUMORS | TISSUE | TUMOR RESPONSE | SPECTRUM ANALYSIS | CANCER | RADIATION | MICROBUBBLE | ENDOTHELIAL-CELL | RADIOTHERAPY | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | HIGH-FREQUENCY ULTRASOUND | Animals | Fibrosarcoma - pathology | Cell Transformation, Neoplastic | Cell Death | Diagnosis, Computer-Assisted - methods | Cell Line, Tumor | Mice | Image Processing, Computer-Assisted - methods | Machine Learning | Fibrosarcoma - diagnostic imaging
Journal Article
Medical Physics, ISSN 0094-2405, 08/2012, Volume 39, Issue 8, pp. 4903 - 4917
Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one... 
quantitative imaging | Cluster analysis | gynaecology | Digital computing or data processing equipment or methods, specially adapted for specific applications | Medical X‐ray imaging | image segmentation | muscle | Film mammography | fuzzy systems | digital mammography | medical image processing | biological organs | support vector machines | Medical imaging | breast density | Medical image segmentation | biological tissues | Radiologists | Mammography | breast cancer risk estimation | Image analysis | Image data processing or generation, in general | Hough transforms | Computer systems utilizing knowledge based models | cancer | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | statistical analysis | CANCER RISK | SCREENING WOMEN | CLASSIFICATION | AGREEMENT | BI-RADS CATEGORIES | TEXTURE | TISSUE COMPOSITION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | PARENCHYMAL PATTERNS | Radiology - methods | Automation | Reproducibility of Results | Humans | Risk | Neoplasms - diagnosis | Models, Statistical | Support Vector Machine | Algorithms | Image Processing, Computer-Assisted | Female | Retrospective Studies | Software | Mammography - methods | Neoplasms - pathology | Breast - pathology | Fuzzy Logic | Cluster Analysis | PATIENTS | MAMMARY GLANDS | NEOPLASMS | BIOMEDICAL RADIOGRAPHY | ALGORITHMS | 60 APPLIED LIFE SCIENCES | WOMEN | IMAGE PROCESSING | FILMS | HEALTH HAZARDS | RISK ASSESSMENT | FUZZY LOGIC | IMAGES | Radiation Imaging Physics
Journal Article
Journal Article
Medical Physics, ISSN 0094-2405, 2015, Volume 42, Issue 10, pp. 5642 - 5653
Current computer-aided detection (CAD) systems for pulmonary nodules in computed tomography (CT) scans have a good performance for relatively small nodules,... 
Cluster analysis | image classification | Segmentation | Pipelines | Digital computing or data processing equipment or methods, specially adapted for specific applications | Databases | Computed tomography | image segmentation | radial basis function networks | Computer aided diagnosis | Image detection systems | Computerised tomographs | medical image processing | detection | support vector machines | Medical imaging | CAD system | Radiologists | lung nodules | Biological material, e.g. blood, urine; Haemocytometers | lung | Inference methods or devices | computerised tomography | Lungs | Image data processing or generation, in general | cancer | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | computed tomography | MANAGEMENT | SCREENING TRIAL | CAD | CANCER | DETECTION SYSTEM | TOMOGRAPHY SCANS | SEGMENTATION | RADIOLOGISTS | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | COMPUTER-AIDED DETECTION | Automation | Diagnosis, Computer-Assisted | Image Processing, Computer-Assisted | Humans | Tomography, X-Ray Computed - methods | Lung Neoplasms - diagnostic imaging | Radiography, Thoracic - methods | Lung - diagnostic imaging | Databases, Factual | DESIGN | IMAGES | PERFORMANCE | LUNGS | SUPPORTS | ALGORITHMS | 60 APPLIED LIFE SCIENCES | COMPUTERIZED TOMOGRAPHY | RESPIRATORS
Journal Article
Medical Physics, ISSN 0094-2405, 10/2012, Volume 39, Issue 10, pp. 5971 - 5980
Purpose: In this work, an approach to computer aided diagnosis (CAD) system is proposed as a decision-making aid in Parkinsonian syndrome (PS) detection. This... 
image classification | image registration | sensitivity analysis | supervised learning | Digital computing or data processing equipment or methods, specially adapted for specific applications | Databases | Registration | Single photon emission computed tomography (SPECT) | medical image processing | support vector machines | Medical imaging | Neuronal imaging | single photon emission computed tomography | diseases | computer aided diagnosis | brain | Testing procedures | Image analysis | support vector machine | Magnetic resonance imaging | Image data processing or generation, in general | Computer systems utilizing knowledge based models | decision making | Nuclear medicine imaging | In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines | neurophysiology | Parkinsonian syndromes | ALZHEIMERS-DISEASE | CLASSIFICATION | DOPAMINE TRANSPORTER SPECT | CIT | ACCURACY | MULTIPLE SYSTEM ATROPHY | IMAGES | I-123-IOFLUPANE | COMPUTER-AIDED DIAGNOSIS | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | PROGRESSIVE SUPRANUCLEAR PALSY | Dopamine Plasma Membrane Transport Proteins - metabolism | Automation | Tomography, Emission-Computed, Single-Photon - methods | Diagnosis, Computer-Assisted | Parkinson Disease - diagnostic imaging | Area Under Curve | Humans | ROC Curve | Parkinson Disease - metabolism | Support Vector Machine
Journal Article