S. Napel

Carlo Tomasi, S. Napel, D. Paik, 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Computed tomography and magnetic resonance colonography (Virtual colonoscopy)

R. Jeffrey, S. Napel, D. Paik, 2000 .

Fast 3D Cardiac Cine MR Imaging

Y Amano, N J Pelc, M T Alley, 1999, Journal of magnetic resonance imaging : JMRI.

Ultra cine-CT for cardiac imaging: where have we been? what lies ahead?

R. E. Rand, S. Napel, D. Boyd, 1987 .

Transparent Rendering of Intraluminal Contrast for 3D Polyp Visualization at CT Colonography

Rong Shi, S. Napel, C. Beaulieu, 2007, Journal of computer assisted tomography.

Targeted 2D/3D registration using ray normalization and a hybrid optimizer.

Joyoni Dey, Sandy Napel, S. Napel, 2006, Medical physics.

Variations in the functional visual field for detection of lung nodules on chest computed tomography: Impact of nodule size, distance, and local lung complexity

L. Ebner, S. Napel, G. Rubin, 2017, Medical physics.

A PROTEIN SIDE-CHAIN PLACEMENT-BASED SOLUTION METHOD FOR UNCLUTTERED SINGLE-IMAGE VISUALIZATION OF THE ABDOMINAL AORTIC VESSEL TREE

Joong-Ho Won, S. Napel, G. Rubin, 2010 .

Efficient Data Archive And Rapid Image Analysis For High Speed CT

Robert G. Gould, Douglas P. Boyd, Sandy Napel, 1986, Other Conferences.

EPI distortion correction for MR-DTI by using texture memory on graphics hardware

Yoshitaka Masutani, Shigeki Aoki, Anthony J. Sherbondy, 2003, CARS.

Virtual endoscopy using perspective volume-rendered three-dimensional sonographic data: technique and clinical applications.

S. Napel, R. Jeffrey, R. Birdwell, 1999, AJR. American journal of roentgenology.

CT colonography: does improved z resolution help computer-aided polyp detection?

Sandy Napel, David S Paik, Christopher F Beaulieu, 2003, Medical physics.

Correction and Quantification of Geometric Distortion in MRI

G. Glover, S. Napel, J. Adler, 2002 .

Quantification of the geometric accuracy of MRI in tissue: a new approach using MRI itself

G. Glover, S. Napel, S. M. Song, 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

Radiomics and Radiogenomics

D. Rubin, S. Napel, L. Xing, 2019, Machine and Deep Learning in Oncology, Medical Physics and Radiology.

Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results.

Olivier Gevaert, Daniel L Rubin, Sandy Napel, 2012, Radiology.

Radiogenomics is the future of treatment response assessment in clinical oncology.

H. Zaidi, S. Napel, I. El Naqa, 2018, Medical physics.

Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study

Olivier Gevaert, Aya Kamaya, Sandy Napel, 2017, Journal of medical imaging.

Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications

Olivier Gevaert, Daniel L Rubin, Sandy Napel, 2017, Radiology.

Cost identification of abdominal aortic aneurysm imaging by using time and motion analyses.

S. Napel, G. Rubin, M. Dake, 2000, Radiology.

Health Policy and Practice

S. Napel, G. Rubin, M. Dake, 2000 .

Measurement of the aorta and its branches with helical CT.

D S Paik, G D Rubin, S Napel, 1998, Radiology.

Semi‐automated pulmonary nodule interval segmentation using the NLST data

Lubomir M. Hadjiiski, Kenny H. Cha, Dmitry Goldgof, 2018, Medical physics.

Erratum: Semi-automated pulmonary nodule interval segmentation using the NLST data.

Kenny H. Cha, R. Gillies, Jayashree Kalpathy-Cramer, 2018, Medical physics.

Computer aided interpretation of medical images

Sandy Napel, David S. Paik, S. Napel, 2002 .

Automated Tracing of the Adventitial Contour of Aortoiliac and Peripheral Arterial Walls in CT Angiography (CTA) to Allow Calculation of Non-calcified Plaque Burden

Geoffrey D. Rubin, Bhargav Raman, Sandy Napel, 2011, Journal of digital imaging.

Thin client architecture in support of remote radiology learning

Geoffrey D. Rubin, Sandy Napel, David S. Paik, 2009, SAC '09.

Quantification of distention in CT colonography: development and validation of three computer algorithms.

R. Jeffrey, S. Napel, J. Yee, 2002, Radiology.

Bone Marrow and Tumor Radiomics at 18 F-FDG PET/CT: Impact on Outcome Prediction in Non–Small Cell Lung Cancer

A. Leung, S. Napel, G. Davidzon, 2019 .

Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine

Maryellen Giger, Sandy Napel, M. Giger, 2016 .

Modeling Perceptual Similarity Measures in CT Images of Focal Liver Lesions

Daniel L. Rubin, Christopher F. Beaulieu, Sandy Napel, 2013, Journal of Digital Imaging.

Carotid disease: automated analysis with cardiac-gated three-dimensional US technique and preliminary results.

Sandy Napel, David S Paik, John A Hossack, 2002, Radiology.

Reconstruction algorithm for polychromatic CT imaging: application to beam hardening correction

Chye Hwang Yan, Sandy Napel, Robert T. Whalen, 2000, IEEE Transactions on Medical Imaging.

Virtual Endoscopy of the Paranasal Sinuses Using Perspective Volume Rendered Helical Sinus Computed Tomography

Sandy Napel, Helmut Ringl, S. Napel, 1997, The Laryngoscope.

Semiautomated quantification of the mass and distribution of vascular calcification with multidetector CT: method and evaluation.

Bhargav Raman, Sandy Napel, Raghav Raman, 2008, Radiology.

Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma

Sandy Napel, Domenico Mastrodicasa, Akshay Jaggi, 2021, Journal of medical imaging.

Serial Scanning and Registration of High Resolution Quantitative Computed Tomography Volume Scans for the Determination of Local Bone Density Changes

Sandy Napel, Robert T. Whalen, Chye H. Yan, 1996 .

Curved-slab maximum intensity projection: method and evaluation.

Sandy Napel, Raghav Raman, Geoffrey D Rubin, 2003, Radiology.

Improved Speed of Bone Removal in Computed Tomographic Angiography Using Automated Targeted Morphological Separation: Method and Evaluation in Computed Tomographic Angiography of Lower Extremity Occlusive Disease

Bhargav Raman, Sandy Napel, Raghav Raman, 2008, Journal of computer assisted tomography.

Femoropopliteal artery centerline interpolation using contralateral shape.

Matus Straka, Sandy Napel, Jarrett Rosenberg, 2007, Medical physics.

CT angiography after 20 years: a transformation in cardiovascular disease characterization continues to advance.

Sandy Napel, Geoffrey D Rubin, Dominik Fleischmann, 2014, Radiology.

Detection of colonic polyps in a phantom model: implications for virtual colonoscopy data acquisition.

C F Beaulieu, G D Rubin, S Napel, 1998, Journal of computer assisted tomography.

Accuracy of detection and measurement of renal calculi: in vitro comparison of three-dimensional spiral CT, radiography, and nephrotomography.

F. G. Sommer, S. Napel, E. Olcott, 1997, Radiology.

Flow measurement of incompressible fluid using divergence-free constraint

S. Napel, S. M. Song, N. Pelc, 1995 .

Prognostic PET 18 F-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non – Small Cell Lung Cancer

Chuong D. Hoang, S. Plevritis, S. Napel, 2012 .

The Medical Segmentation Decathlon

Bjoern H Menze, Amber L. Simpson, AnnetteKopp-Schneider, 2021, Nature Communications.

MRI of pulmonary embolism using Gd-DTPA-polyethylene glycol polymer enhanced 3D fast gradient echo technique in a canine model.

A. Leung, S. Napel, G. Rubin, 1997, Magnetic resonance imaging.

LUCAS NEWS

S. Plevritis, S. Napel, R. Herfkens, 2000 .

Fetal blood velocity waveforms.

S. Napel, W. D. McCallum, C. Williams, 1978, American journal of obstetrics and gynecology.

Semiautomated segmentation of blood vessels using ellipse-overlap criteria: method and comparison to manual editing.

Smadar Shiffman, Sandy Napel, Geoffrey D Rubin, 2003, Medical physics.

Detection of ureteral calculi in patients with suspected renal colic: value of reformatted noncontrast helical CT.

F. G. Sommer, S. Napel, G. Rubin, 1995, AJR. American journal of roentgenology.

A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography

Carlo Tomasi, Burak Acar, Christopher F. Beaulieu, 2001, IEEE Transactions on Medical Imaging.

Can low-dose unenhanced multidetector CT be used for routine evaluation of suspected renal colic?

F. G. Sommer, S. Napel, D. Katz, 2003, AJR. American journal of roentgenology.

Quantitative 3-D diagnostic ultrasound imaging using a modified transducer array and an automated image tracking technique

J.S. Ha, J.A. Hossack, S. Napel, 2002, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

Ruijiang Li, Daniel L Rubin, Sandy Napel, 2018, Breast Cancer Research.

Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

Edward H. Lee, J. Seekins, K. Yeom, 2021, Neurosurgery.

Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics.

Chuong D. Hoang, S. Plevritis, A. Leung, 2017, Oncotarget.

Increased scan pitch for vascular and thoracic spiral CT.

G D Rubin, S Napel, S. Napel, 1995, Radiology.

Automatic Annotation of Radiological Observations in Liver CT Images

Yi Liu, Daniel L. Rubin, Christopher F. Beaulieu, 2012, AMIA.

Assessing operating characteristics of CAD algorithms in the absence of a gold standard.

Kingshuk Roy Choudhury, Sandy Napel, David S Paik, 2010, Medical physics.

GFPT 2-expressing cancer-associated fibroblasts mediate metabolic reprogramming in human lung adenocarcinoma

Sylvia, R. West, M. Rijn, 2018 .

Adaptive border marching algorithm: Automatic lung segmentation on chest CT images

Geoffrey D. Rubin, Jiantao Pu, Sandy Napel, 2008, Comput. Medical Imaging Graph..

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.

Ruijiang Li, Daniel L Rubin, Sandy Napel, 2016, International journal of radiation oncology, biology, physics.

A Mean-Field Theory for Learning the Sch\"{o}nberg Measure of Radial Basis Functions

Y. Ye, S. Napel, M. B. Khuzani, 2020, 2006.13330.

Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer.

A. Leung, S. Napel, G. Davidzon, 2019, Radiology.

GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma.

R. West, M. van de Rijn, A. Gentles, 2018, Cancer Research.

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study

K. Yeom, S. Napel, M. Monje, 2021, Neuro-oncology advances.

Spiral CT of renal artery stenosis: comparison of three-dimensional rendering techniques.

R B Jeffrey, G D Rubin, S Napel, 1994, Radiology.

Three-dimensional spiral computed tomographic angiography: an alternative imaging modality for the abdominal aorta and its branches.

G D Rubin, S Napel, R. S. Mitchell, 1993, Journal of vascular surgery.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Xin Yang, Pheng-Ann Heng, Peter M. Full, 2018, IEEE Transactions on Medical Imaging.

Noise reduction in three‐dimensional phase‐contrast MR velocity measurementsl

N J Pelc, G H Glover, S Napel, 1993, Journal of magnetic resonance imaging : JMRI.

The utility of three-dimensional models in complex microsurgical reconstruction

S. Napel, D. Fleischmann, D. Nguyen, 2020, Archives of plastic surgery.

Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: elucidation with eye tracking.

Martin Tall, Sandy Napel, Brian Harrawood, 2015, Radiology.

Machine Learning Approach to Differentiation of Peripheral Schwannomas and Neurofibromas: A Multi-Center Study.

J. Seekins, K. Yeom, S. Napel, 2021, Neuro-oncology.

Polyp Enhancing Level Set Evolution of Colon Wall: Method and Pilot Study

Ender Konukoglu, Burak Acar, Christopher F. Beaulieu, 2007, IEEE Transactions on Medical Imaging.

Comparison and evaluation of retrospective intermodality brain image registration techniques.

Gerald Q. Maguire, M A Viergever, D L Hill, 1997, Journal of computer assisted tomography.

STS-MIP: a new reconstruction technique for CT of the chest.

G D Rubin, S Napel, S. Napel, 1993, Journal of computer assisted tomography.

Automated generation of curved planar reformations from volume data: method and evaluation.

Sandy Napel, Raghav Raman, Geoffrey D Rubin, 2002, Radiology.

Diagnosis of carotid artery disease: preliminary experience with maximum-intensity-projection spiral CT angiography.

S Napel, S. Napel, D. Enzmann, 1993, AJR. American journal of roentgenology.

Spiral CT creates 3-D neuro, body angiograms.

G D Rubin, S Napel, S. Napel, 1992, Diagnostic imaging.

Automated flight path planning for virtual endoscopy.

D S Paik, C F Beaulieu, R B Jeffrey, 1998, Medical physics.

A large annotated medical image dataset for the development and evaluation of segmentation algorithms pdf

Ronald M. Summers, M. Jorge Cardoso, Lena Maier-Hein, 2019, ArXiv.

Computed tomographic angiography: historical perspective and new state-of-the-art using multi detector-row helical computed tomography.

A. Leung, S. Napel, G. Rubin, 1999, Journal of computer assisted tomography.

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Hayit Greenspan, Daniel L. Rubin, Burak Acar, 2010, Journal of Digital Imaging.

Colon polyp detection using smoothed shape operators: Preliminary results

Afra Zomorodian, Christopher F. Beaulieu, Padma Sundaram, 2008, Medical Image Anal..

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Joyoni Dey, Geoffrey D. Rubin, Burak Acar, 2022 .

Imaging Informatics: Toward Capturing and Processing Semantic Information in Radiology Images

S Napel, D L Rubin, S. Napel, 2010, Yearbook of Medical Informatics.

Uncluttered single-image visualization of the abdominal aortic vessel tree: method and evaluation.

Joong-Ho Won, Sandy Napel, Jarrett Rosenberg, 2009, Medical physics.

Learning-enhanced simulated annealing: method, evaluation, and application to lung nodule registration

Geoffrey D. Rubin, Shaohua Sun, Sandy Napel, 2008, Applied Intelligence.

Registration of lung nodules using a semi-rigid model: method and preliminary results.

Sandy Napel, Geoffrey D Rubin, Feng Zhuge, 2007, Medical physics.

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

A. Rahmim, L. Hadjiiski, M. Muzi, 2020, Tomography.

Fast Fourier projection for MR angiography

S Dunne, S Napel, B K Rutt, 1991, Magnetic resonance in medicine.

Comparison and evaluation of retrospective intermodality image registration techniques

Benoit M. Dawant, Colin Studholme, Robert M. Kessler, 1996, Medical Imaging.

Circle of Willis: evaluation with spiral CT angiography, MR angiography, and conventional angiography.

S. Napel, M. Marks, P. Bracci, 1995, Radiology.

Display modes for CT colonography. Part I. Synthesis and insertion of polyps into patient CT data.

D S Paik, C F Beaulieu, S Napel, 1999, Radiology.

Surface Rendering Versus Volume Rendering In Medical Imaging: Techniques And Applications

William E. Lorensen, Ron Kikinis, John Flynn, 1996, Proceedings of Seventh Annual IEEE Visualization '96.

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Petia Radeva, Laura Igual, Daniel L. Rubin, 2017, IEEE Journal of Biomedical and Health Informatics.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

Daniel L. Rubin, Camille Kurtz, Christopher F. Beaulieu, 2014, J. Biomed. Informatics.

The Medical Segmentation Decathlon

Bjoern H Menze, Amber L. Simpson, Ronald M. Summers, 2022, Nature Communications.

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features

Lubomir M. Hadjiiski, Lin Lu, Yoganand Balagurunathan, 2016, Tomography.

Fast volume segmentation with simultaneous visualization using programmable graphics hardware

Anthony J. Sherbondy, Sandy Napel, Michael Houston, 2003, IEEE Visualization, 2003. VIS 2003..

A Comprehensive Descriptor of Shape: Method and Application to Content-Based Retrieval of Similar Appearing Lesions in Medical Images

Daniel L. Rubin, Christopher F. Beaulieu, Sandy Napel, 2012, Journal of Digital Imaging.

Managing Biomedical Image Metadata for Search and Retrieval of Similar Images

Daniel L. Rubin, Christopher F. Beaulieu, Sandy Napel, 2011, Journal of Digital Imaging.

Adaptive local window for level set segmentation of CT and MRI liver lesions

Daniel L. Rubin, Claude B. Sirlin, Assaf Hoogi, 2016, Medical Image Anal..

Content-based image retrieval in radiology: analysis of variability in human perception of similarity

Daniel L Rubin, Sandy Napel, Jarrett Rosenberg, 2015, Journal of medical imaging.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

Daniel L. Rubin, Adrien Depeursinge, Camille Kurtz, 2014, Medical Image Anal..

Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT

Adrien Depeursinge, Sandy Napel, Daniel Rubin, 2014, IEEE Transactions on Medical Imaging.

Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval.

Hayit Greenspan, Sandy Napel, Daniel Rubin, 2012, Medical physics.

ConTrack: finding the most likely pathways between brain regions using diffusion tractography.

Anthony J. Sherbondy, Sandy Napel, Brian A Wandell, 2008, Journal of vision.

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

Robert J. Gillies, Dmitry B. Goldgof, Hao Yang, 2016, Journal of Digital Imaging.

Automated temporal tracking and segmentation of lymphoma on serial CT examinations.

Hayit Greenspan, Daniel L Rubin, Sandy Napel, 2011, Medical physics.

Accuracy of a remote eye tracker for radiologic observer studies: effects of calibration and recording environment.

Martin Tall, Sandy Napel, Kingshuk Roy Choudhury, 2012, Academic radiology.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images

Daniel L. Rubin, Sebastian Echegaray, Sandy Napel, 2018, Journal of Digital Imaging.

Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines

Daniel L Rubin, Sandy Napel, Sebastian Echegaray, 2020, Journal of medical imaging.

Phase unwrapping of MR phase images using Poisson equation

Gary H. Glover, Norbert J. Pelc, Samuel Moon-Ho Song, 1995, IEEE Trans. Image Process..

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

Olivier Gevaert, Aya Kamaya, Sandy Napel, 2015, Journal of medical imaging.

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

D. Rubin, S. Napel, B. Daniel, 2018, Breast Cancer Research.

Medical image segmentation using analysis of isolable-contour maps

S. Napel, S. Napel, G. Rubin, 2000, IEEE Transactions on Medical Imaging.

Medial axis registration of supine and prone CT colonography data

S. Napel, S. Napel, J. Yee, 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets

Edward A. Lee, R. Gillies, S. Napel, 2020, Nature Machine Intelligence.

1 Prognostic PET 18 F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer

Chuong D. Hoang, S. Napel, G. Davidzon, 2012 .

Stair-step artifacts with single versus multiple detector-row helical CT.

D S Paik, C F Beaulieu, G D Rubin, 2000, Radiology.

Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images.

A. Kesselman, S. Napel, O. Gevaert, 2020, Radiology. Imaging cancer.

Projection presaturation. II. Single-shot localization of multiple regions of interest

Brian K. Rutt, Sandy Napel, S. Napel, 1990 .

Machine learning with multimodal data for COVID-19

M. Giger, S. Napel, O. Gevaert, 2023, Heliyon.

A Radiomics Approach to Analyze Cardiac Alterations in Hypertension

Oscar Camara, Miguel Ángel González Ballester, Steffen E. Petersen, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

A directional distance aided method for medical image segmentation.

Sandy Napel, Geoffrey Rubin, Feng Zhuge, 2007, Medical physics.

Arti ﬁ cial intelligence and machine learning in cancer imaging

C. E. Kahn, S. Napel, E. Sala, 2022 .

A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI pdf

Oscar Camara, Miguel Ángel González Ballester, Gerard Sanroma, 2017, STACOM@MICCAI.

Volumetric analysis of volumetric data: achieving a paradigm shift.

G D Rubin, S Napel, A. Leung, 1996, Radiology.

Uncluttered Single-Image Visualization of Vascular Structures Using GPU and Integer Programming

Joong-Ho Won, Sungroh Yoon, Yongkweon Jeon, 2013, IEEE Transactions on Visualization and Computer Graphics.

Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions.

Ilke Tunali, Matthew B Schabath, Sandy Napel, 2019, Medical physics.

A radiogenomic dataset of non-small cell lung cancer

Chuong D. Hoang, S. Plevritis, A. Leung, 2018, Scientific Data.

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge

Lubomir M. Hadjiiski, Gabriel Bernardino, Hichem Frigui, 2021, IEEE Transactions on Medical Imaging.

A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions pdf

Lei Xing, Yinyu Ye, Sandy Napel, 2020, ArXiv.

Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features

Michael McNitt-Gray, Sandy Napel, Sarah A. Mattonen, 2020, Tomography.

On the Feasibility of Predicting Radiological Observations from Computational Imaging Features of Liver Lesions in CT Scans

Yi Liu, Daniel L. Rubin, Christopher F. Beaulieu, 2011, 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology.

A Learning Method for Automated Polyp Detection

Carlo Tomasi, Burak Acar, Christopher F. Beaulieu, 2001, MICCAI.

Small Cell Lung Cancer − Nonwith Major Oncogenomic Alterations in Patients with Resected F-FDG Uptake Imaging Features Are Associated 18 Prognostic PET

Chuong D. Hoang, S. Plevritis, D. Rubin, 2012 .

A Rapid Segmentation-Insensitive “Digital Biopsy” Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non–Small Cell Lung Cancer

Olivier Gevaert, Sandy Napel, Sebastian Echegaray, 2016, Tomography.

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Maria A. Zuluaga, M. Jorge Cardoso, Suyash P. Awate, 2023, ArXiv.

Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

Steven D Chang, K. Yeom, D. Rubin, 2015, Science Translational Medicine.

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

R. Steenbakkers, M. Hatt, M. Desseroit, 2016, Radiology.