Katharina Höbel

According to our database, Katharina Höbel authored at least 23 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology.

Tiago Gonçalves, Dagoberto Pulido-Arias, Julian Willett, Katharina Viktoria Hoebel, Mason C. Cleveland, Syed Rakin Ahmed, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Jaime S. Cardoso, Christopher P. Bridge, Albert E. Kim

CoRR, 2024

Addressing Catastrophic Forgetting by Modulating Global Batch Normalization Statistics for Medical Domain Expansion.

Sharut Gupta, Ken Chang, Liangqiong Qu, Aakanksha Rana, Syed Rakin Ahmed, Mehak Aggarwal, Nishanth Thumbavanam Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay B. Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer, Praveer Singh

Proceedings of the Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine, 2024

2023

FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.

Bin Deng, Hanxue Gu, Hongmin Zhu, Ken Chang, Katharina Viktoria Hoebel, Jay B. Patel, Jayashree Kalpathy-Cramer, Stefan A. Carp

IEEE Trans. Medical Imaging, 2023

A generalized framework to predict continuous scores from medical ordinal labels.

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

CoRR, 2023

A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging.

Jay B. Patel, Syed Rakin Ahmed, Ken Chang, Praveer Singh, Mishka Gidwani, Katharina Hoebel, Albert E. Kim, Christopher P. Bridge, Chung-Jen Teng, Xiaomei Li, Gongwen Xu, Megan McDonald, Ayal Aizer, Wenya Linda Bi, K. Ina Ly, Bruce Rosen, Priscilla K. Brastianos, Raymond Y. Huang, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer

Proceedings of the Machine Learning for Healthcare Conference, 2023

2022

Improving the repeatability of deep learning models with Monte Carlo dropout.

Andréanne Lemay, Katharina Hoebel, Christopher P. Bridge, Brian Befano, Silvia De Sanjosé, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer

npj Digit. Medicine, 2022

Is this good enough? On expert perception of brain tumor segmentation quality.

Katharina Hoebel, Christopher P. Bridge, Sara Ahmed, Oluwatosin Akintola, Caroline Chung, Raymond Y. Huang, Jason Johnson, Albert E. Kim, K. Ina Ly, Ken Chang, Jay B. Patel, Marco Pinho, Tracy Batchelor, Bruce R. Rosen, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer

Proceedings of the Medical Imaging 2022: Image Perception, 2022

Do I know this? segmentation uncertainty under domain shift.

Katharina Hoebel, Christopher P. Bridge, Andréanne Lemay, Ken Chang, Jay B. Patel, Bruce Rosen, Jayashree Kalpathy-Cramer

Proceedings of the Medical Imaging 2022: Image Processing, 2022

Fair Conformal Predictors for Applications in Medical Imaging.

Charles Lu, Andréanne Lemay, Ken Chang, Katharina Höbel, Jayashree Kalpathy-Cramer

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

DeepNeuro: an open-source deep learning toolbox for neuroimaging.

Andrew Beers, James M. Brown, Ken Chang, Katharina Hoebel, Jay B. Patel, K. Ina Ly, Sara M. Tolaney, Priscilla K. Brastianos, Bruce R. Rosen, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer

Neuroinformatics, 2021

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

Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Alexandra Daza, Catalina Gómez Caballero, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Verónica Vilaplana, Hugh McHugh, Gonzalo D. Maso Talou, Alan Wang, Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam 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, Élodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Lladó, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas J. Tustison, Craig H. Meyer, Nisarg A. Shah, Sanjay N. Talbar, Marc-André Weber, Abhishek Mahajan, András Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel S. Marcus, Aikaterini Kotrotsou, Rivka Colen, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Bjoern H. Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

CoRR, 2021

Monte Carlo dropout increases model repeatability.

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

CoRR, 2021

Evaluating subgroup disparity using epistemic uncertainty in mammography.

Charles Lu, Andréanne Lemay, Katharina Hoebel, Jayashree Kalpathy-Cramer

CoRR, 2021

Addressing catastrophic forgetting for medical domain expansion.

Sharut Gupta, Praveer Singh, Ken Chang, Liangqiong Qu, Mehak Aggarwal, Nishanth Thumbavanam Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay B. Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer

CoRR, 2021

2020

Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging.

Matthew D. Li, Ken Chang, Ben Bearce, Connie Y. Chang, Ambrose J. Huang, J. Peter Campbell, James M. Brown, Praveer Singh, Katharina Viktoria Hoebel, Deniz Erdogmus, Stratis Ioannidis, William E. Palmer, Michael F. Chiang, Jayashree Kalpathy-Cramer

npj Digit. Medicine, 2020

The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions.

Sharut Gupta, Praveer Singh, Ken Chang, Mehak Aggarwal, Nishanth Thumbavanam Arun, Liangqiong Qu, Katharina Hoebel, Jay B. Patel, Mishka Gidwani, Ashwin Vaswani, Daniel L. Rubin, Jayashree Kalpathy-Cramer

CoRR, 2020

Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging.

Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh, Ken Chang, Mehak Aggarwal, Bryan Chen, Katharina Hoebel, Sharut Gupta, Jay B. Patel, Mishka Gidwani, Julius Adebayo, Matthew D. Li, Jayashree Kalpathy-Cramer

CoRR, 2020

Assessing the validity of saliency maps for abnormality localization in medical imaging.

Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh, Ken Chang, Katharina Viktoria Hoebel, Jay B. Patel, Mishka Gidwani, Jayashree Kalpathy-Cramer

CoRR, 2020

An exploration of uncertainty information for segmentation quality assessment.

Katharina Hoebel, Vincent Andrearczyk, Andrew Beers, Jay B. Patel, Ken Chang, Adrien Depeursinge, Henning Müller, Jayashree Kalpathy-Cramer

Proceedings of the Medical Imaging 2020: Image Processing, 2020

Federated Learning for Breast Density Classification: A Real-World Implementation.

Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas J. Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina Viktoria Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel L. Rubin, Jayashree Kalpathy-Cramer

Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020

Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks.

Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Bruce R. Rosen, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019

Give me (un)certainty - An exploration of parameters that affect segmentation uncertainty.

Katharina Hoebel, Ken Chang, Jay B. Patel, Praveer Singh, Jayashree Kalpathy-Cramer

CoRR, 2019

2018

DeepNeuro: an open-source deep learning toolbox for neuroimaging.

Andrew Beers, James M. Brown, Ken Chang, Katharina Hoebel, Elizabeth R. Gerstner, Bruce R. Rosen, Jayashree Kalpathy-Cramer

CoRR, 2018