Craig Jones

Assistant Research Professor

Johns Hopkins University

United States

Research Interests

Explore related searches

Professors in United States Professors in Medical Imaging Medical Imaging professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Craig Jones is an Assistant Research Professor at Johns Hopkins University in the United States. His research primarily focuses on advanced imaging techniques and artificial intelligence applications in neuroscience, as evidenced by his recent publications on topics such as neuroendoscopic guidance, fMRI analysis, and 3D video reconstruction for neurosurgery. He is also involved in collaborative efforts to enhance cancer detection methodologies through federated learning.

Articles (13)

Year:

2025

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Collaborators (20)

Umang Pandey

Universidad Carlos III de Madrid - Campus de Leganés

SPAIN

Craig Jones

Research Interests

Explore related searches

Contact this professor

About

Articles (13)

Self-Supervised Feature Detection and 3D Reconstruction for Real-Time Neuroendoscopic Guidance

Atlas-Based Labeling of Resting-State fMRI

Real-Time 3-D Video Reconstruction for Guidance of Transventricular Neurosurgery

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.

Optical Coherence Tomography Angiography, Artificial Intelligence, and the Missing Capillaries.

Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.

Interrater Reliability of Cervical Neural Foraminal Stenosis Using Traditional and Splayed Reconstructions: Analysis of One Hundred Scans.

Author Correction: Federated learning enables big data for rare cancer boundary detection.

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging.

Federated learning enables big data for rare cancer boundary detection.

Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images

Active shape model registration of ocular structures in computed tomography images

Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology In Training

Collaborators (20)

Umang Pandey

Maarten Wijnenga

Sebastian van der Voort

Gianluca Brugnara

Hongwei Li

Tobias Weiss

John Buatti

Junghoon Lee

Ali Uneri

Fabio Ynoe Moraes

Sean Benson

Bryan Allen

Ashok Srinivasan

Yading Yuan

Konstantinos Kamnitsas

Michael Weller

Nicholas Theodore

Prasad Vagdargi

Panagiotis Tsiganos

Anas Belouali

Make your next move.