PhD Student at Johns Hopkins University • NSF Fellow • NIH Clinical Center Special Volunteer

Publications

This contains a (hopefully) up-to-date list of all relevant publications. For metrics, please see Google Scholar.

Journals

  1. Zuo, L., Liu, Y., Xue, Y., Dewey, B. E., Remedios, S. W., Hays, S. P., Bilgel, M., Mowry, E. M., Newsome, S. D., Calabresi, P. A., Resnick, S. M., Prince, J. L., & Carass, A. (2023). HACA3: A unified approach for multi-site MR image harmonization. Computerized Medical Imaging and Graphics, 109, 102285.
  2. Han, S., Remedios, S. W., Schär, M., Carass, A., & Prince, J. L. (2023). ESPRESO: An algorithm to estimate the slice profile of a single magnetic resonance image. Magnetic Resonance Imaging, 98, 155–163.
  3. Bown, C. W., Khan, O. A., Liu, D., Remedios, S. W., Pechman, K. R., Terry, J. G., Nair, S., Davis, L. T., Landman, B. A., Gifford, K. A., & others. (2023). Enlarged perivascular space burden associations with arterial stiffness and cognition. Neurobiology of Aging, 124, 85–97.
  4. Chou, Y., Chang, C., Remedios, S. W., Butman, J. A., Chan, L., & Pham, D. L. (2022). Automated classification of resting-state fMRI ICA components using a deep Siamese Network. Frontiers in Neuroscience, 16.
  5. Remedios, L. W., Lingam, S., Remedios, S. W., Gao, R., Clark, S. W., Davis, L. T., & Landman, B. A. (2021). Comparison of convolutional neural networks for detecting large vessel occlusion on computed tomography angiography. Medical Physics, 48(10), 6060–6068.
  6. Bermudez, C., Remedios, S. W., Ramadass, K., McHugo, M., Heckers, S., Huo, Y., & Landman, B. A. (2020). Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning. Journal of Medical Imaging, 7(6), 1–22.
  7. Schilling, K. G., Petit, L., Rheault, F., Remedios, S., Pierpaoli, C., Anderson, A. W., Landman, B. A., & Descoteaux, M. (2020). Brain connections derived from diffusion MRI tractography can be highly anatomically accurate—if we know where white matter pathways start, where they end, and where they do not go. Brain Structure and Function, 225(8), 2387–2402.
  8. Remedios, S. W., Roy, S., Bermudez, C., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2019). Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation. Medical Physics, 47(1), 89–98.

Conference Publications

  1. Remedios, S. W., Han, S., Zuo, L., Carass, A., Pham, D. L., Prince, J. L., & Dewey, B. E. (2023). Self-Supervised Super-Resolution for Anisotropic MR Images with and Without Slice Gap. In J. M. Wolterink, D. Svoboda, C. Zhao, & V. Fernandez (Eds.), Simulation and Synthesis in Medical Imaging (pp. 118–128). Springer Nature Switzerland.
  2. Remedios, S. W., Dewey, B. E., Xue, Y., Zuo, L., Cassard, S. D., Koch, C., Fishman, A., Prince, J. L., Mowry, E. M., & Newsome, S. D. (2023). Cautions in Anisotropy: Thick Slices and Slice Gaps in 2D Magnetic Resonance Acquisition Tarnish Volumetrics. International Journal of Multiple Sclerosis Care.
  3. Dewey, B. E., Zuo, L., Remedios, S. W., Xue, Y., Cassard, S. D., Koch, C., Fishman, A., Carass, A., Prince, J. L., Mowry, E. M., & Newsome, S. D. (2023). Compliance with CMSC MRI Guidelines in a Multi-Center, Pragmatic, Randomized Clinical Trial: Improvements over Time. International Journal of Multiple Sclerosis Care.
  4. Zuo, L., Hays, S. P., Dewey, B. E., Remedios, S. W., Xue, Y., Cassard, S. D., Koch, C., Fishman, A., Carass, A., Prince, J. L., Mowry, E. M., & Newsome, S. D. (2023). Inconsistent MR Acquisition in Longitudinal Volumetric Analysis: Impacts and Solutions. International Journal of Multiple Sclerosis Care.
  5. Hays, S. P., Zuo, L., Dewey, B. E., Remedios, S. W., Xue, Y., Cassard, S. D., Koch, C., Fishman, A., Carass, A., Calabresi, P. A., Prince, J. L., Mowry, E. M., & Newsome, S. D. (2023). Quantifying contrast differences among MR images used in clinical studies. International Journal of Multiple Sclerosis Care.
  6. Dewey, B. E., Fishman, A., Cassard, S. D., Zuo, L., Remedios, S. W., Xue, Y., Koch, C., Carass, A., Prince, J. L., Mowry, E. M., & Newsome, S. D. (2023). Measuring MRIs Differences Between Sites: Design of a Traveling Subject Study in MS. International Journal of Multiple Sclerosis Care.
  7. Xue, Y., Dewey, B. E., Zuo, L., Remedios, S. W., Hays, S. P., Cassard, S. D., Koch, C., Fishman, A., Carass, A., Calabresi, P. A., Prince, J. L., Mowry, E. M., & Newsome, S. D. (2023). Synthesizing Missing MRI Sequences to Improve Processing Images in Multiple Sclerosis. International Journal of Multiple Sclerosis Care.
  8. Remedios, S. W., Dewey, B. E., Carass, A., Pham, D. L., & Prince, J. L. (2023). A deep generative prior for high-resolution isotropic MR head slices. Medical Imaging 2023: Image Processing.
  9. Xue, Y., Zuo, L., Remedios, S. W., Dewey, B. E., Duan, P., Liu, Y., Zhang, R., Newsome, S. D., Mowry, E. M., Carass, A., & Prince, J. L. (2023). Unsupervised quality assurance for brain MR image rigid registration using latent shape representation. Medical Imaging 2023: Image Processing.
  10. Remedios, S. W., Han, S., Xue, Y., Carass, A., Tran, T. D., Pham, D. L., & Prince, J. L. (2022). Deep filter bank regression for super-resolution of anisotropic MR brain images. International Conference on Medical Image Computing and Computer-Assisted Intervention, 613–622.
  11. Xue, Y., Dewey, B. E., Zuo, L., Han, S., Carass, A., Duan, P., Remedios, S. W., Pham, D. L., Saidha, S., Calabresi, P. A., & others. (2022). Bi-directional Synthesis of Pre-and Post-contrast MRI via Guided Feature Disentanglement. International Workshop on Simulation and Synthesis in Medical Imaging, 55–65.
  12. Remedios, L. W., Cai, L. Y., Hansen, C. B., Remedios, S. W., & Landman, B. (2022). Efficient quality control with mixed CT and CTA datasets. Medical Imaging 2022: Image Processing, 12032, 93–99.
  13. Chou, Y., Remedios, S. W., Butman, J. A., & Pham, D. L. (2022). Automatic classification of MRI contrasts using a deep Siamese network and one-shot learning. Medical Imaging 2022: Image Processing, 12032, 110–114.
  14. Tohidi, P., Remedios, S. W., Greenman, D. L., Shao, M., Han, S., Dewey, B. E., Reinhold, J. C., Chou, Y.-Y., Pham, D. L., Prince, J. L., & Carass, A. (2022). Multiple Sclerosis brain lesion segmentation with different architecture ensembles. Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 12036, 578–585.
  15. Remedios, S. W., Han, S., Dewey, B. E., Pham, D. L., Prince, J. L., & Carass, A. (2021). Joint Image and Label Self-super-Resolution. Simulation and Synthesis in Medical Imaging, 14–23.
  16. Han, S., Remedios, S. W., Carass, A., Schär, M., & Prince, J. L. (2021). MR Slice Profile Estimation by Learning to Match Internal Patch Distributions. Information Processing in Medical Imaging, 108–119.
  17. Bown, C. W., Khan, O. A., Liu, D., Remedios, S., Pechman, K. R., Schrag, M., Davis, L. T., Terry, J. G., Nair, S., Carr, J. J., & others. (2021). Perivascular space volumes relate to arterial stiffness and cognition. 2021 Alzheimer’s Association International Conference.
  18. Remedios, S. W., Butman, J. A., Landman, B. A., & Pham, D. L. (2020). Federated gradient averaging for multi-site training with momentum-based optimizers. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 170–180.
  19. Bermudez, C., Blaber, J., Remedios, S. W., Reynolds, J. E., Lebel, C., McHugo, M., Heckers, S., Huo, Y., & Landman, B. A. (2020). Generalizing deep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning. Medical Imaging 2020: Image Processing, 11313, 111–118.
  20. Remedios, S., Wu, Z., Bermudez, C., Kerley, C. I., Roy, S., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2020). Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection. Medical Imaging 2020: Image Processing, 11313, 66–75.
  21. Nath, V., Schilling, K. G., Remedios, S., Bayrak, R. G., Gao, Y., Blaber, J. A., Huo, Y., Landman, B. A., & Anderson, A. W. (2019). Learning 3D White Matter Microstructure from 2D Histology. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 186–190.
  22. Nath, V., Parvathaneni, P., Hansen, C. B., Hainline, A. E., Bermudez, C., Remedios, S., Blaber, J. A., Schilling, K. G., Lyu, I., Janve, V., Gao, Y., Stepniewska, I., Rogers, B. P., Newton, A. T., Davis, L. T., Luci, J., Anderson, A. W., & A., L. B. (2019). Inter-scanner harmonization of high angular resolution DW-MRI using null space deep learning. International Conference on Medical Image Computing and Computer-Assisted Intervention, 193–201.
  23. Remedios, S., Roy, S., Blaber, J., Bermudez, C., Nath, V., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2019). Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury. Medical Imaging 2019: Image Processing, 10949, 68–75.
  24. Nath, V., Remedios, S., Parvathaneni, P., Hansen, C. B., Bayrak, R. G., Bermudez, C., Blaber, J. A., Schilling, K. G., Janve, V. A., Gao, Y., Huo, Y., Lyu, I., Williams, O., Resnick, S., Beason-Held, L., Rogers, B. P., Stepniewska, I., Anderson, A. W., & Landman, B. A. (2019). Harmonizing 1.5T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators . Medical Imaging 2019: Image Processing, 10949, 173–182.
  25. Remedios, S., Pham, D. L., Butman, J. A., & Roy, S. (2018). Classifying magnetic resonance image modalities with convolutional neural networks. Medical Imaging 2018: Computer-Aided Diagnosis, 10575, 558–563.

Talks

  1. Self-Supervised Super-Resolution for Anisotropic MR Images with and Without Slice Gap. (2023).
  2. A deep generative prior for high-resolution isotropic MR head slices. (2023).
  3. Joint image and label self-super-resolution. (2021).
  4. Federated gradient averaging for multi-site training with momentum-based optimizers. (2020).
  5. Obtaining a trained 2D deep learning model with 3D weak volume labels using multiple instance learning for CT hemorrhage detection. (2020).
  6. Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection. (2020).
  7. Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury. (2019).
  8. Classifying magnetic resonance image modalities with convolutional neural networks. (2018).
  9. Deep Learning for Classification of Magnetic Resonance Brain Images. (2017).