DeepNeuro: an open-source deep learning toolbox for neuroimaging. - Andrew Beers; James M. Brown 001; Ken Chang; Katharina Hoebel; Elizabeth R. Gerstner; Bruce R. Rosen; Jayashree Kalpathy-Cramer (CoRR, 2018)

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)

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 (BrainLes@MICCAI (2), 2020)

Federated Learning for Breast Density Classification: A Real-World Implementation. - Holger R. Roth; Ken Chang; Praveer Singh; Nir Neumark; Wenqi Li 001; Vikash Gupta; Sharut Gupta; Liangqiong Qu; Alvin Ihsani; Bernardo C. Bizzo; Yuhong Wen; Varun Buch; et al. (DART/DCL@MICCAI, 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 (Image Processing, 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)

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; et al. (CoRR, 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)

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 001; Praveer Singh; Katharina Viktoria Hoebel; Deniz Erdogmus; Stratis Ioannidis; William E. Palmer; et al. (npj Digit. Medicine, 2020)

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; et al. (CoRR, 2021)

Evaluating subgroup disparity using epistemic uncertainty in mammography. - Charles Lu 001; Andréanne Lemay; Katharina Hoebel; Jayashree Kalpathy-Cramer (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)

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; et al. (CoRR, 2021)

DeepNeuro: an open-source deep learning toolbox for neuroimaging. - Andrew Beers; James M. Brown 001; 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)

Fair Conformal Predictors for Applications in Medical Imaging. - Charles Lu 001; Andréanne Lemay; Ken Chang; Katharina Höbel; Jayashree Kalpathy-Cramer (AAAI, 2022)

Do I know this? segmentation uncertainty under domain shift. - Katharina Hoebel; Christopher P. Bridge; Andréanne Lemay; Ken Chang; Jay B. Patel; Bruce Rosen 001; Jayashree Kalpathy-Cramer (Image Processing, 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; et al. (Image Perception, Observer Performance, and Technology Assessment, 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)

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; et al. (MLHC, 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)

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)

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; et al. (AIPAD/PILM@MICCAI, 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 001; Christopher P. Bridge; Albert E. Kim (CoRR, 2024)