Peter A. Pinto
Affiliations:- National Institutes of Health, Bethesda, MD, USA
According to our database1,
Peter A. Pinto
authored at least 42 papers
between 2010 and 2024.
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Bibliography
2024
IEEE J. Biomed. Health Informatics, November, 2024
Location-Based Radiology Report-Guided Semi-Supervised Learning for Prostate Cancer Detection.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
2023
Automated classification of intravenous contrast enhancement phase of CT scans using residual networks.
Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, San Diego, 2023
2021
IEEE Access, 2021
No-Reference Image Quality Assessment Of T2-Weighted Magnetic Resonance Images In Prostate Cancer Patients.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021
2020
Improving detection of prostate cancer foci via information fusion of MRI and temporal enhanced ultrasound.
Int. J. Comput. Assist. Radiol. Surg., 2020
Improving Dense Pixelwise Prediction of Epithelial Density Using Unsupervised Data Augmentation for Consistency Regularization.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Deep Learning Framework for Epithelium Density Estimation in Prostate Multi-Parametric Magnetic Resonance Imaging.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
2019
Deep neural maps for unsupervised visualization of high-grade cancer in prostate biopsies.
Int. J. Comput. Assist. Radiol. Surg., 2019
Int. J. Comput. Assist. Radiol. Surg., 2019
2018
Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound.
IEEE Trans. Medical Imaging, 2018
MRI-Guided Robotically Assisted Focal Laser Ablation of the Prostate Using Canine Cadavers.
IEEE Trans. Biomed. Eng., 2018
Int. J. Comput. Assist. Radiol. Surg., 2018
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018
A Mixed Reality Guidance System for Robot Assisted Laparoscopic Radical Prostatectomy.
Proceedings of the OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, - and - Skin Image Analysis, 2018
Learning from Noisy Label Statistics: Detecting High Grade Prostate Cancer in Ultrasound Guided Biopsy.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018
2017
Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection.
Int. J. Comput. Assist. Radiol. Surg., 2017
Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations.
Int. J. Comput. Assist. Radiol. Surg., 2017
Proceedings of the Medical Imaging 2017: Image-Guided Procedures, 2017
Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
Biopsy-guided learning with deep convolutional neural networks for Prostate Cancer detection on multiparametric MRI.
Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017
2016
Int. J. Comput. Assist. Radiol. Surg., 2016
Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study.
Int. J. Comput. Assist. Radiol. Surg., 2016
Proceedings of the Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, 27 February, 2016
An MRI guided system for prostate laser ablation with treatment planning and multi-planar temperature monitoring.
Proceedings of the Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, 27 February, 2016
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016
2015
Augmenting MRI-transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study.
Int. J. Comput. Assist. Radiol. Surg., 2015
Nucleus detection using gradient orientation information and linear least squares regression.
Proceedings of the Medical Imaging 2015: Digital Pathology, 2015
Ultrasound-Based Detection of Prostate Cancer Using Automatic Feature Selection with Deep Belief Networks.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 2015
Efficient Hilbert transform-based alternative to Tofts physiological models for representing MRI dynamic contrast-enhanced images in computer-aided diagnosis of prostate cancer.
Proceedings of the Medical Imaging 2015: Computer-Aided Diagnosis, 2015
2014
Proceedings of the Medical Imaging 2014: Digital Pathology, 2014
Proceedings of the Clinical Image-Based Procedures. Translational Research in Medical Imaging, 2014
2013
Int. J. Comput. Assist. Radiol. Surg., 2013
A prostate cancer computer-aided diagnosis system using multimodal magnetic resonance imaging and targeted biopsy labels.
Proceedings of the Medical Imaging 2013: Computer-Aided Diagnosis, 2013
2012
Gaussian Process Inference for Estimating Pharmacokinetic Parameters of Dynamic Contrast-Enhanced MR Images.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, 2012
A statistical model-based technique for accounting for prostate gland deformation in endorectal coil-based MR imaging.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
2011
An MRI-Compatible Robotic System With Hybrid Tracking for MRI-Guided Prostate Intervention.
IEEE Trans. Biomed. Eng., 2011
2010
Multi-parametric MRI-pathologic correlation of prostate cancer using tracked biopsies.
Proceedings of the Medical Imaging 2010: Visualization, 2010
Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates.
Proceedings of the Prostate Cancer Imaging. Computer-Aided Diagnosis, 2010