Perry J. Pickhardt

According to our database1, Perry J. Pickhardt authored at least 37 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.
CoRR, 2024

2023
Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain.
IEEE Trans. Medical Imaging, June, 2023

Improved ascites segmentation with bladder identification using anatomical location residual U-Net.
Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, 2023

2022
Vector textures derived from higher order derivative domains for classification of colorectal polyps.
Vis. Comput. Ind. Biomed. Art, 2022

An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography.
Sensors, 2022

Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity.
CoRR, 2022

Automated Assessment of Renal Calculi in Serial Computed Tomography Scans.
Proceedings of the Applications of Medical Artificial Intelligence, 2022

Cardiovascular disease and all-cause mortality risk prediction from abdominal CT using deep learning.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022

A vector representation of local image contrast patterns for lesion classification.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022

2021
3D deep learning for computer-aided detection of serrated polyps in CT colonography.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

2020
3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography.
IEEE Trans. Medical Imaging, 2020

Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection.
CoRR, 2020

Cross-domain Medical Image Translation by Shared Latent Gaussian Mixture Model.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Multilevel UNet for pancreas segmentation from non-contrast CT scans through domain adaptation.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

Integration of optical and virtual colonoscopy images for enhanced classification of colorectal polyps.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

Performance investigation of deep learning vs. classifier for polyp differentiation via texture features.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

Accurately identifying vertebral levels in large datasets.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

Deformation robust texture features for polyp classification via CT colonography.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

2019
Multi-scale characterizations of colon polyps via computed tomographic colonography.
Vis. Comput. Ind. Biomed. Art, 2019

A statistical analysis of oral tagging in CT colonography and its impact on flat polyp detection and characterization.
Proceedings of the Medical Imaging 2019: Image Perception, 2019

Improved polyp classification by inclusion of the surrounding colon wall textures.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019

A local geometrical metric-based model for polyp classification.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019

2018
Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018

2017
A study of oral contrast coating on the surface of polyps: an implication for computer-aided detection and classification of polyps.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017

False positive reduction for wall thickness-based detection of colonic flat polyps via CT colonography.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017

Deep learning of contrast-coated serrated polyps for computer-aided detection in CT colonography.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017

2016
Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography.
IEEE Trans. Medical Imaging, 2016

An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies.
Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February, 2016

2015
Distance weighted 'inside disc' classifier for computer-aided diagnosis of colonic polyps.
Proceedings of the Medical Imaging 2015: Computer-Aided Diagnosis, 2015

2014
Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography.
Int. J. Comput. Assist. Radiol. Surg., 2014

An adaptive approach to centerline extraction for CT colonography using MAP-EM segmentation and distance field.
Proceedings of the Medical Imaging 2014: Computer-Aided Diagnosis, 2014

2013
Haustral Fold Segmentation With Curvature-Guided Level Set Evolution.
IEEE Trans. Biomed. Eng., 2013

Registration of Temporally Separated CT Colonography Cases.
Proceedings of the Abdominal Imaging. Computation and Clinical Applications, 2013

2012
Automatic colonic fold segmentation for computed tomography colonography.
Proceedings of the Medical Imaging 2012: Computer-Aided Diagnosis, 2012

2010
Projection-based features for reducing false positives in computer-aided detection of colonic polyps in CT colonography.
Proceedings of the Medical Imaging 2010: Computer-Aided Diagnosis, 2010

2006
Hybrid segmentation of colon filled with air and opacified fluid for CT colonography.
IEEE Trans. Medical Imaging, 2006

Automatic colonic polyp detection using multi-objective evolutionary techniques.
Proceedings of the Medical Imaging 2006: Image Processing, 2006


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