Cam Bermudez
Medical Image Analysis and Statistical Interpretation Laboratory
Biomedical Engineering, Electrical Engineering, & Computer Science
Vanderbilt University
Nashville, TN 37235, USA
About me
I am an M.D. - Ph.D. student at the Vanderbilt University Medical Scientist Training Program. I finished my Ph.D. in the Spring of 2020 in the Department of Biomedical Engineering under the mentorship of Bennett Landman, Ph.D. My career goal is to transform healthcare by driving innovation in engineering and translating it into clinical practice to improve patient health. To accomplish this, I develop quantitative tools from data science and image processing in order to better understand disease and optimize treatment strategies. My current research is focused on the development and validation of quantitative biomarkers from clinical and imaging datasets to inform medical decision making. I believe that leveraging information from rich datasets has the potential to drive personalized medicine and improve patient care.
Research Interests
Medical Image Processing · Deep Learning · Imaging Biomarkers · Clinical Neurology · Personalized Medicine
Education
2014 - May 2021
2010 - 2014
Medical Scientist Training Program (MSTP) -- M.D. - Ph.D. Biomedical Engineering
Vanderbilt University, Nashville, TN
B.S.E. in Bioengineering & Mathematics
University of Pennsylvania, Philadelphia, PA
Honors & Awards
Member of Alpha Omega Alpha (AOA) Honor Society (2020)
Future in Neurological Research Scholarship (2020) -- American Academy of Neurology
Best Conference Paper Award (2019) – SPIE Medical Imaging: Image Processing (Sponsored by 12Sigma)
Predoctoral Training Fellowship (2018) – American Heart Association
Future Clinical Researcher in Neurology (2017) – American Academy of Neurology
Medical Student Diversity Scholarship (2017) – American Academy of Neurology
Recipient of Nvidia’s GPU Grant Program (2017, 2018)
1st Place in the School of Engineering Senior Design Competition (2014) -- https://www.youtube.com/watch?v=jKZcBx5XsdA
Select Publications
2020
Bermudez, C., Blaber, J., Remedios, S.W., Reynolds, J.E., Lebel, C., McHugo, M., Heckers, S., Huo, Y., Landman, B.A.. Generalizingdeep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning.. SPIE 2020: Medical Imaging.2020, Mar; 11313(1): 113130L.
Bermudez, C., Christman, S., Hao, L., Landman, B.A., Boyd, B., Albert, K., Woodward, N., Shokouhi, S., Vega, J., Andrews, P.,Taylor, W.D.. Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adultdepression. Translational Psychiatry. 2020, Sep; 10(1): 1-11. Cited in PubMed; PMID: 32948749.
Li, Y., Zhang, H., Bermudez, C., Chen, Y., Landman, B.A., Vorobeychik, Y.. Anatomical context protects deep learning fromadversarial perturbations in medical imaging.. Neurocomputing. 2020, Feb; 379(1): 370-378.
Remedios, S.W., Roy, S., Bermudez, C., Patel, M.B., Butman, J.A., Landman, B.A., Pham, D.L.. Distributed deep learning acrossmulti-site datasets for generalized CT hemorrhage segmentation.. Medical Physics. 2020, Jan; 47(1): 89-98.
2019
Huo, Y., Xu, Z., Xiong, Y., Aboud, K., Parvathaneni, P., Bao, S., Bermudez, C., Resnick, S.M., Cutting, L.E., & Landman, B. A. (2019). 3d whole brain segmentation using spatially localized atlas network tiles. NeuroImage, 194, 105-119.
Bermudez, C., Plassard, A. J., Chaganti, S., Huo, Y., Aboud, K. E., Cutting, L. E., ... & Landman, B. A. (2019). Anatomical context improves deep learning on the brain age estimation task. Magnetic Resonance Imaging.
Bao, S., Bermudez, C., Huo, Y., Parvathaneni, P., Rodriguez, W., Resnick, S. M., ... & Lyu, I. (2019). Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields. Magnetic resonance imaging, 59, 143-152.
Nath, V., Remedios, S., Parvathaneni, P., Hansen, C. B., Bayrak, R. G., Bermudez, C., ... & Huo, Y. (2019, March). Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators. In Medical Imaging 2019: Image Processing (Vol. 10949, p. 109490O). International Society for Optics and Photonics.
Remedios, S., Roy, S., Blaber, J., Bermudez, C., Nath, V., Patel, M. B., ... & Pham, D. L. (2019, March). Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury. In Medical Imaging 2019: Image Processing (Vol. 10949, p. 109490A). International Society for Optics and Photonics.
Bermudez, C., Rodriguez, W., Huo, Y., Hainline, A. E., Li, R., Shults, R., ... & Landman, B. A. (2019, March). Towards machine learning prediction of deep brain stimulation (DBS) intra-operative efficacy maps. In Medical Imaging 2019: Image Processing (Vol. 10949, p. 1094922). International Society for Optics and Photonics.
Huo, Y., Terry, J. G., Wang, J., Nath, V., Bermudez, C., Bao, S., ... & Landman, B. A. (2019, March). Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning. In Medical Imaging 2019: Image Processing (Vol. 10949, p. 1094917). International Society for Optics and Photonics.
2018
Huo, Y., Xu, Z., Bao, S., Bermudez, C., Moon, H., Parvathaneni, P., ... & Landman, B. A. (2018). Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks. IEEE transactions on medical imaging, 38(5), 1185-1196.
Nath, V., Parvathaneni, P., Hansen, C. B., Hainline, A. E., Bermudez, C., Remedios, S., ... & Gao, Y. (2018, September). Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 193-201). Springer, Cham.
Huo, Y., Xu, Z., Aboud, K., Parvathaneni, P., Bao, S., Bermudez, C., ... & Landman, B. A. (2018, September). Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 698-705). Springer, Cham.
Tomlinson, S. B., Khambhati, A. N., Bermudez, C., Kamens, R. M., Heuer, G. G., Porter, B. E., & Marsh, E. D. (2018). Alterations of network synchrony after epileptic seizures: An analysis of post-ictal intracranial recordings in pediatric epilepsy patients. Epilepsy research, 143, 41-49.
Bermudez, C., Plassard, A.J., Davis, L.T., Newton, A.T., Resnick, S.M., and Landman, B.A. (2018) Learning implicit brain MRI manifolds with deep learning. SPIE Medical Imaging. International Society for Optics and Photonics. 2018. Accepted for Publication. [arXiv]
Huo, Y., Xu, Z., Bao, S., Bermudez, C., Plassard, A.J., Liu, J., Yao, Y., Assad, A., Abramson, R.G., and Landman, B.A. (2018) Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks. In SPIE Medical Imaging, International Society for Optics and Photonics, 2018. Accepted for Publication. [arXiv]
2017
Chaganti, S., Robinson, J. R., Bermudez, C., Lasko, T., Mawn, L. A., & Landman, B. A. (2017). EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 373-381). Springer, Cham. [Web]
2016
Tomlinson S, Bermudez C, Conley C, Brown M, Porter BE and Marsh ED (2016). Spatiotemporal mapping of interictal spike propagation: a novel methodology applied to pediatric intracranial EEG recordings. Front. Neurol. 7:229. doi: 10.3389/fneur.2016.00229 [Web]
Wise, E. S., Gadomski, S. P., Ilg, A. M., Bermudez, C., Chan, E. W., Izmaylov, M. L., ... & Hocking, K. M. (2016). Independent Preoperative Predictors of Prolonged Length of Stay after Laparoscopic Appendectomy in Patients Over 30 Years of Age: Experience from a Single Institution. The American Surgeon, 82(11), 1092-1097. [Web]
2015
Zhu, X., Dubey, D., Bermudez, C., & Porter, B. E. (2015). Suppressing cAMP response element-binding protein transcription shortens the duration of status epilepticus and decreases the number of spontaneous seizures in the pilocarpine model of epilepsy. Epilepsia, 56(12), 1870-1878. doi:10.1111/epi.13211 [Web]