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Katharina Höbel
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2020 – today
- 2024
- [c8]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:
Addressing Catastrophic Forgetting by Modulating Global Batch Normalization Statistics for Medical Domain Expansion. AIPAD/PILM@MICCAI 2024: 57-72 - [i14]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:
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology. CoRR abs/2404.16397 (2024) - 2023
- [j4]Bin Deng
, Hanxue Gu, Hongmin Zhu
FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction. IEEE Trans. Medical Imaging 42(8): 2439-2450 (2023) - [c7]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:
A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging. MLHC 2023: 565-587 - [i13]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:
A generalized framework to predict continuous scores from medical ordinal labels. CoRR abs/2305.19097 (2023) - 2022
- [j3]Andréanne Lemay, Katharina Hoebel
Improving the repeatability of deep learning models with Monte Carlo dropout. npj Digit. Medicine 5 (2022) - [c6]Charles Lu, Andréanne Lemay, Ken Chang, Katharina Höbel, Jayashree Kalpathy-Cramer:
Fair Conformal Predictors for Applications in Medical Imaging. AAAI 2022: 12008-12016 - [c5]Katharina Hoebel, Christopher P. Bridge, Andréanne Lemay, Ken Chang, Jay B. Patel, Bruce Rosen, Jayashree Kalpathy-Cramer:
Do I know this? segmentation uncertainty under domain shift. Medical Imaging: Image Processing 2022 - [c4]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:
Is this good enough? On expert perception of brain tumor segmentation quality. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2022 - [i12]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:
Improving the repeatability of deep learning models with Monte Carlo dropout. CoRR abs/2202.07562 (2022) - 2021
- [j2]Andrew Beers, James M. Brown
DeepNeuro: an open-source deep learning toolbox for neuroimaging. Neuroinformatics 19(1): 127-140 (2021) - [i11]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:
Addressing catastrophic forgetting for medical domain expansion. CoRR abs/2103.13511 (2021) - [i10]Charles Lu, Andréanne Lemay, Katharina Hoebel, Jayashree Kalpathy-Cramer:
Evaluating subgroup disparity using epistemic uncertainty in mammography. CoRR abs/2107.02716 (2021) - [i9]Charles Lu, Andréanne Lemay, Ken Chang, Katharina Hoebel, Jayashree Kalpathy-Cramer:
Fair Conformal Predictors for Applications in Medical Imaging. CoRR abs/2109.04392 (2021) - [i8]Andréanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer:
Monte Carlo dropout increases model repeatability. CoRR abs/2111.06754 (2021) - [i7]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:
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. CoRR abs/2112.10074 (2021) - 2020
- [j1]Matthew D. Li
Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging. npj Digit. Medicine 3 (2020) - [c3]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:
Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks. BrainLes@MICCAI (2) 2020: 228-240 - [c2]Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu
Federated Learning for Breast Density Classification: A Real-World Implementation. DART/DCL@MICCAI 2020: 181-191 - [c1]Katharina Hoebel, Vincent Andrearczyk, Andrew Beers, Jay B. Patel, Ken Chang, Adrien Depeursinge, Henning Müller
An exploration of uncertainty information for segmentation quality assessment. Medical Imaging: Image Processing 2020: 113131K - [i6]Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh, Ken Chang, Katharina Viktoria Hoebel, Jay B. Patel, Mishka Gidwani, Jayashree Kalpathy-Cramer:
Assessing the validity of saliency maps for abnormality localization in medical imaging. CoRR abs/2006.00063 (2020) - [i5]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:
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. CoRR abs/2008.02766 (2020) - [i4]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:
Federated Learning for Breast Density Classification: A Real-World Implementation. CoRR abs/2009.01871 (2020) - [i3]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:
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions. CoRR abs/2011.08096 (2020)
2010 – 2019
- 2019
- [i2]Katharina Hoebel, Ken Chang, Jay B. Patel, Praveer Singh, Jayashree Kalpathy-Cramer:
Give me (un)certainty - An exploration of parameters that affect segmentation uncertainty. CoRR abs/1911.06357 (2019) - 2018
- [i1]Andrew Beers, James M. Brown, Ken Chang, Katharina Hoebel, Elizabeth R. Gerstner, Bruce R. Rosen, Jayashree Kalpathy-Cramer:
DeepNeuro: an open-source deep learning toolbox for neuroimaging. CoRR abs/1808.04589 (2018)
Coauthor Index
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