Alert button
Picture for Jayashree Kalpathy-Cramer

Jayashree Kalpathy-Cramer

Alert button

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

Aug 28, 2023
Guillermo Lorenzo, Syed Rakin Ahmed, David A. Hormuth II, Brenna Vaughn, Jayashree Kalpathy-Cramer, Luis Solorio, Thomas E. Yankeelov, Hector Gomez

Viaarxiv icon

A generalized framework to predict continuous scores from medical ordinal labels

May 30, 2023
Katharina V. Hoebel, Andreanne Lemay, John Peter Campbell, Susan Ostmo, Michael F. Chiang, Christopher P. Bridge, Matthew D. Li, Praveer Singh, Aaron S. Coyner, Jayashree Kalpathy-Cramer

Figure 1 for A generalized framework to predict continuous scores from medical ordinal labels
Figure 2 for A generalized framework to predict continuous scores from medical ordinal labels
Figure 3 for A generalized framework to predict continuous scores from medical ordinal labels
Figure 4 for A generalized framework to predict continuous scores from medical ordinal labels
Viaarxiv icon

MONAI: An open-source framework for deep learning in healthcare

Nov 04, 2022
M. Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myronenko, Can Zhao, Dong Yang, Vishwesh Nath, Yufan He, Ziyue Xu, Ali Hatamizadeh, Andriy Myronenko, Wentao Zhu, Yun Liu, Mingxin Zheng, Yucheng Tang, Isaac Yang, Michael Zephyr, Behrooz Hashemian, Sachidanand Alle, Mohammad Zalbagi Darestani, Charlie Budd, Marc Modat, Tom Vercauteren, Guotai Wang, Yiwen Li, Yipeng Hu, Yunguan Fu, Benjamin Gorman, Hans Johnson, Brad Genereaux, Barbaros S. Erdal, Vikash Gupta, Andres Diaz-Pinto, Andre Dourson, Lena Maier-Hein, Paul F. Jaeger, Michael Baumgartner, Jayashree Kalpathy-Cramer, Mona Flores, Justin Kirby, Lee A. D. Cooper, Holger R. Roth, Daguang Xu, David Bericat, Ralf Floca, S. Kevin Zhou, Haris Shuaib, Keyvan Farahani, Klaus H. Maier-Hein, Stephen Aylward, Prerna Dogra, Sebastien Ourselin, Andrew Feng

Figure 1 for MONAI: An open-source framework for deep learning in healthcare
Figure 2 for MONAI: An open-source framework for deep learning in healthcare
Figure 3 for MONAI: An open-source framework for deep learning in healthcare
Figure 4 for MONAI: An open-source framework for deep learning in healthcare
Viaarxiv icon

Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling

Sep 12, 2022
Sourav Kumar, A. Lakshminarayanan, Ken Chang, Feri Guretno, Ivan Ho Mien, Jayashree Kalpathy-Cramer, Pavitra Krishnaswamy, Praveer Singh

Figure 1 for Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
Figure 2 for Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
Figure 3 for Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
Viaarxiv icon

Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction

Jul 12, 2022
Charles Lu, Syed Rakin Ahmed, Praveer Singh, Jayashree Kalpathy-Cramer

Figure 1 for Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Figure 2 for Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Figure 3 for Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Figure 4 for Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Viaarxiv icon

Three Applications of Conformal Prediction for Rating Breast Density in Mammography

Jun 23, 2022
Charles Lu, Ken Chang, Praveer Singh, Jayashree Kalpathy-Cramer

Figure 1 for Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Figure 2 for Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Figure 3 for Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Figure 4 for Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Viaarxiv icon

Improving the repeatability of deep learning models with Monte Carlo dropout

Feb 15, 2022
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Brian Befano, Silvia De Sanjosé, Diden Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer

Figure 1 for Improving the repeatability of deep learning models with Monte Carlo dropout
Figure 2 for Improving the repeatability of deep learning models with Monte Carlo dropout
Figure 3 for Improving the repeatability of deep learning models with Monte Carlo dropout
Figure 4 for Improving the repeatability of deep learning models with Monte Carlo dropout
Viaarxiv icon

Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating

Feb 10, 2022
Ikbeom Jang, Garrison Danley, Ken Chang, Jayashree Kalpathy-Cramer

Figure 1 for Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating
Figure 2 for Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating
Viaarxiv icon

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results

Dec 19, 2021
Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat AK, Sarahi Rosas-González, Illyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-Andr Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

Figure 1 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 2 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 3 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 4 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Viaarxiv icon

Monte Carlo dropout increases model repeatability

Nov 12, 2021
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer

Figure 1 for Monte Carlo dropout increases model repeatability
Figure 2 for Monte Carlo dropout increases model repeatability
Figure 3 for Monte Carlo dropout increases model repeatability
Figure 4 for Monte Carlo dropout increases model repeatability
Viaarxiv icon