Katharina Hoebel
Katharina Hoebel
MD, PhD-candidate Harvard-MIT Division of Health Sciences and Technology
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Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging
N Arun, N Gaw, P Singh, K Chang, M Aggarwal, B Chen, K Hoebel, ...
Radiology: Artificial Intelligence 3 (6), 2021
Federated learning for breast density classification: A real-world implementation
HR Roth, K Chang, P Singh, N Neumark, W Li, V Gupta, S Gupta, L Qu, ...
Domain adaptation and representation transfer, and distributed and …, 2020
A disintegrin and metalloprotease 17 dynamic interaction sequence, the sweet tooth for the human interleukin 6 receptor
S Düsterhöft, K Höbel, M Oldefest, J Lokau, GH Waetzig, A Chalaris, ...
Journal of Biological Chemistry 289 (23), 16336-16348, 2014
Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging
MD Li, K Chang, B Bearce, CY Chang, AJ Huang, JP Campbell, ...
NPJ digital medicine 3 (1), 1-9, 2020
Machine learning models can detect aneurysm rupture and identify clinical features associated with rupture
MA Silva, J Patel, V Kavouridis, T Gallerani, A Beers, K Chang, KV Hoebel, ...
World Neurosurgery 131, e46-e51, 2019
DeepNeuro: an open-source deep learning toolbox for neuroimaging
A Beers, J Brown, K Chang, K Hoebel, J Patel, KI Ly, SM Tolaney, ...
Neuroinformatics 19 (1), 127-140, 2021
Multi-institutional assessment and crowdsourcing evaluation of deep learning for automated classification of breast density
K Chang, AL Beers, L Brink, JB Patel, P Singh, NT Arun, KV Hoebel, ...
Journal of the American College of Radiology 17 (12), 1653-1662, 2020
Radiomics repeatability pitfalls in a scan-rescan MRI study of glioblastoma
KV Hoebel, JB Patel, AL Beers, K Chang, P Singh, JM Brown, MC Pinho, ...
Radiology: Artificial Intelligence 3 (1), e190199, 2020
An exploration of uncertainty information for segmentation quality assessment
K Hoebel, V Andrearczyk, A Beers, J Patel, K Chang, A Depeursinge, ...
Medical Imaging 2020: Image Processing 11313, 381-390, 2020
Assessing the validity of saliency maps for abnormality localization in medical imaging
NT Arun, N Gaw, P Singh, K Chang, KV Hoebel, J Patel, M Gidwani, ...
arXiv preprint arXiv:2006.00063, 2020
Balloon catheter-based radiofrequency ablation monitoring in porcine esophagus using optical coherence tomography
WCY Lo, N Uribe-Patarroyo, K Hoebel, K Beaudette, M Villiger, ...
Biomedical Optics Express 10 (4), 2067-2089, 2019
Addressing catastrophic forgetting for medical domain expansion
S Gupta, P Singh, K Chang, L Qu, M Aggarwal, N Arun, A Vaswani, ...
arXiv preprint arXiv:2103.13511, 2021
Fair conformal predictors for applications in medical imaging
C Lu, A Lemay, K Chang, K Höbel, J Kalpathy-Cramer
Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 12008 …, 2022
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation--Analysis of Ranking Metrics and Benchmarking Results
R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ...
arXiv preprint arXiv:2112.10074, 2021
Segmentation, survival prediction, and uncertainty estimation of gliomas from multimodal 3D MRI using selective kernel networks
J Patel, K Chang, K Hoebel, M Gidwani, N Arun, S Gupta, M Aggarwal, ...
International MICCAI Brainlesion Workshop, 228-240, 2020
Evaluating subgroup disparity using epistemic uncertainty in mammography
C Lu, A Lemay, K Hoebel, J Kalpathy-Cramer
arXiv preprint arXiv:2107.02716, 2021
Deep feature transfer between localization and segmentation tasks
SY Hu, A Beers, K Chang, K Höbel, JP Campbell, D Erdogumus, ...
arXiv preprint arXiv:1811.02539, 2018
Improving the repeatability of deep learning models with Monte Carlo dropout
A Lemay, K Hoebel, CP Bridge, B Befano, S De Sanjosé, D Egemen, ...
arXiv preprint arXiv:2202.07562, 2022
QU-BraTS: MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results
R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ...
Journal of Machine Learning for Biomedical Imaging 1, 2022
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
S Gupta, P Singh, K Chang, M Aggarwal, N Arun, L Qu, K Hoebel, J Patel, ...
arXiv preprint arXiv:2011.08096, 2020
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