Jayashree Kalpathy-Cramer

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Professor

University of Colorado School of Medicine

United States

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About

Professor Jayashree Kalpathy-Cramer is a faculty member at the University of Colorado Denver, University of Colorado Medicine, in the United States. Her research areas encompass cervical cancer screening strategies, deep learning applications in medical imaging, and the integration of artificial intelligence in healthcare. Recent articles highlight her work on the evaluation of AI in clinical settings and the analysis of MRI for brain tumor research.

Recent Grants

Grant: Close

Informatics Tools for Optimized Imaging Biomarkers for Cancer Research&Discovery

Open Date: 2014-09-01

Close Date: 2019-08-31

Grant: Close

Informatics Tools for Optimized Imaging Biomarkers for Cancer Research&Discovery

Open Date: 2014-09-01

Close Date: 2019-08-31

Grant: Close

Automated retinopathy of prematurity classification using machine learning

Open Date: 2013-09-01

Close Date: 2015-08-31

Grant: Close

Automated retinopathy of prematurity classification using machine learning

Open Date: 2013-09-01

Close Date: 2015-08-31

Grant: Close

Quantitative MRI of Glioblastoma Response

Open Date: 2011-05-06

Close Date: 2017-04-30

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2021

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2021

Collaborators (19)

Wendy Rogers

Macquarie University

AUSTRALIA

Jayashree Kalpathy-Cramer

Research Interests

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About

Recent Grants

Articles (24)

Environment scan of generative AI infrastructure for clinical and translational science.

Initial evaluation of a new cervical screening strategy combining human papillomavirus genotyping and automated visual evaluation: the Human Papillomavirus–Automated Visual Evaluation Consortium

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

Generalizable deep neural networks for image quality classification of cervical images.

A review of deep learning for brain tumor analysis in MRI.

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts

Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge

EyeLiner: A Deep Learning Pipeline for Longitudinal Image Registration Using Fundus Landmarks.

Multi-institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB.

Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences.

The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset.

A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges.

Epidemiologic Evaluation of Retinopathy of Prematurity Severity in a Large Telemedicine Program in India Using Artificial Intelligence

Artificial Intelligence and Glaucoma: Going Back to Basics

Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data

Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models

Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT

Breast Cancer Risk Assessment in the AI Era: The Importance of Model Validation in Ethnically Diverse Cohorts

Improving the repeatability of deep learning models with Monte Carlo dropout

Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels

A Fully Automated Deep Learning Pipeline for Multi–Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans

RSNA-MICCAI Panel Discussion: Machine Learning for Radiology from Challenges to Clinical Applications

The RSNA International COVID-19 Open Radiology Database (RICORD)

Collaborators (19)

Wendy Rogers

John Campbell

Tinashe Mutsvangwa

John Osborne

DESPINA KONTOS

Stacy Carter

Stratis Ioannidis

Maciej Bobowicz

Mishka Gidwani

Luciano Prevedello

Savvas Nicolaou

Christophe Gerard Lambert

Tyler Richards

Niranjan Karnik

Manolis Tsiknakis

Savannah Partridge

Malik Y. Kahook

Martijn Pieter Anton Starmans

Leonid Roshkovan

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