Katharina Hoebel headshot

Katharina Hoebel, MD, PhD

Research Fellow in Biomedical Informatics
Yu Lab

Katharina Hoebel is a Postdoctoral Research Fellow in the lab of Kun-Hsing Yu in the Department of Biomedical Informatics at Harvard Medical School in Boston. Previously, she completed her PhD in Medical Engineering and Medical Physics in the Harvard-MIT Division of Health Sciences and Technology at MIT, under the guidance of Prof. Jayashree Kalpathy-Cramer. From 2023 to 2024, she was a Research Fellow in the Department of Data Science at Dana-Farber Cancer Institute in Boston. Katharina also holds an MD from Heidelberg University Medical School and a BS in Physics with a minor in Computer Science from Kiel University in Germany.

Katharina’s research focuses on developing AI models tailored to the specific needs of our healthcare systems, grounded in the belief that making a meaningful impact on healthcare providers and patients requires the development of models guided by domain expertise and a user-centered approach. Currently, her primary focus lies in leveraging the capabilities of ML models to emulate complex patterns within medical data. Drawing inspiration from work in drug discovery and interpretability, she aims to employ these models to enhance our understanding of complex diseases such as cancer.

Abnormal vascular structure and function within brain metastases is linked to pembrolizumab resistance.
Authors: Kim AE, Lou KW, Giobbie-Hurder A, Chang K, Gidwani M, Hoebel K, Patel JB, Cleveland MC, Singh P, Bridge CP, Ahmed SR, Bearce BA, Liu W, Fuster-Garcia E, Lee EQ, Lin NU, Overmoyer B, Wen PY, Nayak L, Cohen JV, Dietrich J, Eichler A, Heist R, Krop I, Lawrence D, Ligibel J, Tolaney S, Mayer E, Winer E, Perrino CM, Summers EJ, Mahar M, Oh K, Shih HA, Cahill DP, Rosen BR, Yen YF, Kalpathy-Cramer J, Martinez-Lage M, Sullivan RJ, Brastianos PK, Emblem KE, Gerstner ER.
Neuro Oncol
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Not without Context-A Multiple Methods Study on Evaluation and Correction of Automated Brain Tumor Segmentations by Experts.
Authors: Hoebel KV, Bridge CP, Kim A, Gerstner ER, Ly IK, Deng F, DeSalvo MN, Dietrich J, Huang R, Huang SY, Pomerantz SR, Vagvala S, Rosen BR, Kalpathy-Cramer J.
Acad Radiol
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Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.
Authors: Hoebel KV, Bridge CP, Ahmed S, Akintola O, Chung C, Huang RY, Johnson JM, Kim A, Ly KI, Chang K, Patel J, Pinho M, Batchelor TT, Rosen BR, Gerstner ER, Kalpathy-Cramer J.
Radiol Artif Intell
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Structural and functional vascular dysfunction within brain metastases is linked to pembrolizumab inefficacy.
Authors: Kim AE, Lou KW, Giobbie-Hurder A, Chang K, Gidwani M, Hoebel K, Patel JB, Cleveland MC, Singh P, Bridge CP, Ahmed SR, Bearce BA, Liu W, Fuster-Garcia E, Lee EQ, Lin NU, Overmoyer B, Wen PY, Nayak L, Cohen JV, Dietrich J, Eichler A, Heist R, Krop I, Lawrence D, Ligibel J, Tolaney S, Mayer E, Winer E, Perrino CM, Summers EJ, Mahar M, Oh K, Shih HA, Cahill DP, Rosen BR, Yen YF, Kalpathy-Cramer J, Martinez-Lage M, Sullivan RJ, Brastianos PK, Emblem KE, Gerstner ER.
bioRxiv
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FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.
Authors: Deng B, Gu H, Zhu H, Chang K, Hoebel KV, Patel JB, Kalpathy-Cramer J, Carp SA.
IEEE Trans Med Imaging
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Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models.
Authors: Gidwani M, Chang K, Patel JB, Hoebel KV, Ahmed SR, Singh P, Fuller CD, Kalpathy-Cramer J.
Radiology
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Improving the repeatability of deep learning models with Monte Carlo dropout.
Authors: Lemay A, Hoebel K, Bridge CP, Befano B, De Sanjosé S, Egemen D, Rodriguez AC, Schiffman M, Campbell JP, Kalpathy-Cramer J.
NPJ Digit Med
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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.
Authors: Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, Dätwyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, Ganesh C, Wagner B, Yu FF, Fei B, Madhuranthakam AJ, Maldjian JA, Daza L, Gómez C, Arbeláez P, Dai C, Wang S, Reynaud H, Mo Y, Angelini E, Guo Y, Bai W, Banerjee S, Pei L, Ak M, Rosas-González S, Zemmoura I, Tauber C, Vu MH, Nyholm T, Löfstedt T, Ballestar LM, Vilaplana V, McHugh H, Maso Talou G, Wang A, Patel J, Chang K, Hoebel K, Gidwani M, Arun N, Gupta S, Aggarwal M, Singh P, Gerstner ER, Kalpathy-Cramer J, Boutry N, Huard A, Vidyaratne L, Rahman MM, Iftekharuddin KM, Chazalon J, Puybareau E, Tochon G, Ma J, Cabezas M, Llado X, Oliver A, Valencia L, Valverde S, Amian M, Soltaninejad M, Myronenko A, Hatamizadeh A, Feng X, Dou Q, Tustison N, Meyer C, Shah NA, Talbar S, Weber MA, Mahajan A, Jakab A, Wiest R, Fathallah-Shaykh HM, Nazeri A, Milchenko M, Marcus D, Kotrotsou A, Colen R, Freymann J, Kirby J, Davatzikos C, Menze B, Bakas S, Gal Y, Arbel T.
J Mach Learn Biomed Imaging
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Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging.
Authors: Arun N, Gaw N, Singh P, Chang K, Aggarwal M, Chen B, Hoebel K, Gupta S, Patel J, Gidwani M, Adebayo J, Li MD, Kalpathy-Cramer J.
Radiol Artif Intell
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DeepNeuro: an open-source deep learning toolbox for neuroimaging.
Authors: Beers A, Brown J, Chang K, Hoebel K, Patel J, Ly KI, Tolaney SM, Brastianos P, Rosen B, Gerstner ER, Kalpathy-Cramer J.
Neuroinformatics
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